diff --git a/.cursor/rules/general-rule.mdc b/.cursor/rules/general-rule.mdc
index 01ef0ac64..c130f8259 100644
--- a/.cursor/rules/general-rule.mdc
+++ b/.cursor/rules/general-rule.mdc
@@ -5,7 +5,6 @@ alwaysApply: true
 ---
 ## Rules to Follow
 
-- You must always commit your changes whenever you update code. 
-- You must always try and write code that is well documented. (self or commented is fine)
-- You must only work on a single feature at a time.
-- You must explain your decisions thouroughly to the user.
\ No newline at end of file
+You always prefer to use branch development. Before writing any code - you create a feature branch to hold those changes. 
+
+After you are done - provide instructions in a "MERGE.md" file that explains how to merge the changes back to main with both a GitHub PR route and a GitHub CLI route.
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 8b7ec41e3..c34f599ca 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,9 @@ uv.lock
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
+*.pyc
+*.pyo
+*.pyd
 *.py[cod]
 *$py.class
 
@@ -163,3 +166,11 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 .vercel
+
+# Node.js
+node_modules
+.next
+
+# PDF filess
+*.pdf
+
diff --git a/.vercelignore b/.vercelignore
new file mode 100644
index 000000000..0b902433c
--- /dev/null
+++ b/.vercelignore
@@ -0,0 +1,17 @@
+# Ignore development files
+*.log
+*.tmp
+.DS_Store
+.vscode/
+.idea/
+
+# Ignore large files that aren't needed for deployment
+*.ipynb
+*.md
+.git/
+.gitignore
+
+# Keep only essential files for deployment
+!api/
+!frontend/
+!vercel.json
diff --git a/Accessing_GPT_4_1_nano_Like_a_Developer.ipynb b/Accessing_GPT_4_1_nano_Like_a_Developer.ipynb
deleted file mode 100644
index a8e63ace4..000000000
--- a/Accessing_GPT_4_1_nano_Like_a_Developer.ipynb
+++ /dev/null
@@ -1,738 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kQt-gyAYUbm3"
-      },
-      "source": [
-        "### Using the OpenAI Library to Programmatically Access GPT-4.1-nano!"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PInACkIWUhOd"
-      },
-      "source": [
-        "In order to get started, we'll need to provide our OpenAI API Key - detailed instructions can be found [here](https://github.com/AI-Maker-Space/Interactive-Dev-Environment-for-LLM-Development#-setting-up-keys-and-tokens)!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ecnJouXnUgKv",
-        "outputId": "c6c25850-395d-4cbf-9d26-bfe9253d1711"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import openai\n",
-        "import getpass\n",
-        "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Please enter your OpenAI API Key: \")\n",
-        "openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "T1pOrbwSU5H_"
-      },
-      "source": [
-        "### Our First Prompt\n",
-        "\n",
-        "You can reference OpenAI's [documentation](https://platform.openai.com/docs/api-reference/chat) if you get stuck!\n",
-        "\n",
-        "Let's create a `ChatCompletion` model to kick things off!\n",
-        "\n",
-        "There are three \"roles\" available to use:\n",
-        "\n",
-        "- `developer`\n",
-        "- `assistant`\n",
-        "- `user`\n",
-        "\n",
-        "OpenAI provides some context for these roles [here](https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages)\n",
-        "\n",
-        "Let's just stick to the `user` role for now and send our first message to the endpoint!\n",
-        "\n",
-        "If we check the documentation, we'll see that it expects it in a list of prompt objects - so we'll be sure to do that!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "iy_LEPNEMVvC"
-      },
-      "outputs": [],
-      "source": [
-        "from openai import OpenAI\n",
-        "\n",
-        "client = OpenAI()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ofMwuUQOU4sf",
-        "outputId": "7db141d5-7f7a-4f82-c9ff-6eeafe65cfa6"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "ChatCompletion(id='chatcmpl-BUc2UgMuVcdtDkvmU1KdbRuq7z4bd', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Great question! LangChain and LlamaIndex (formerly known as GPT Index) are both popular frameworks designed to facilitate building applications with large language models (LLMs), but they serve different primary purposes and have distinct features.\\n\\n**LangChain:**\\n\\n- **Purpose:** A comprehensive framework for developing LLM-powered applications, especially those involving chaining multiple prompts, tools, and components.\\n- **Core Features:**\\n  - Supports building complex prompt workflows, including chaining, few-shot prompting, and memory.\\n  - Facilitates integration with various LLM providers (OpenAI, Hugging Face, etc.).\\n  - Enables the use of external tools and APIs within LLM applications.\\n  - Designed for creating chatbots, question-answering systems, and more complex AI workflows.\\n- **Use Cases:** Conversational agents, personalized assistants, multimodal workflows, and applications requiring advanced prompt engineering.\\n\\n---\\n\\n**LlamaIndex (GPT Index):**\\n\\n- **Purpose:** A toolkit focused on indexing, retrieving, and querying large document datasets efficiently using LLMs.\\n- **Core Features:**\\n  - Builds indices over document collections (e.g., PDFs, text files, web pages).\\n  - Supports retrieval-augmented generation (RAG), where relevant documents are fetched to inform LLM responses.\\n  - Provides easy-to-use components for document ingestion, indexing, and querying.\\n  - Optimized for building applications that require knowledge base retrieval and question-answering over large external datasets.\\n- **Use Cases:** Document search, knowledge base creation, retrieval-augmented question answering, and information retrieval tasks.\\n\\n---\\n\\n### **Summary of the Main Difference:**\\n\\n| Aspect | LangChain | LlamaIndex (GPT Index) |\\n|---|---|---|\\n| **Primary Focus** | Building complex LLM workflows and applications | Efficient retrieval and querying of large document collections with LLMs |\\n| **Use Cases** | Chatbots, conversational AI, multi-step workflows | Knowledge bases, document QA, retrieval-augmented generation |\\n| **Features** | Prompt chaining, tool integration, memory management | Document indexing, fast retrieval, external knowledge integration |\\n\\n---\\n\\n**In essence:**  \\n- Use **LangChain** if you want to build sophisticated, multi-step applications with LLMs that may involve tools, memory, and layered prompts.  \\n- Use **LlamaIndex** if your goal is to index large amounts of documents and enable efficient retrieval and question-answering over them, often in a retrieval-augmented setup.\\n\\nBoth can be complementary; some projects utilize both frameworks together for different parts of their architecture.', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None))], created=1746635762, model='gpt-4.1-nano-2025-04-14', object='chat.completion', service_tier='default', system_fingerprint='fp_eede8f0d45', usage=CompletionUsage(completion_tokens=522, prompt_tokens=19, total_tokens=541, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "YOUR_PROMPT = \"What is the difference between LangChain and LlamaIndex?\"\n",
-        "\n",
-        "client.chat.completions.create(\n",
-        "    model=\"gpt-4.1-nano\",\n",
-        "    messages=[{\"role\" : \"user\", \"content\" : YOUR_PROMPT}]\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IX-7MnFhVNoT"
-      },
-      "source": [
-        "As you can see, the prompt comes back with a tonne of information that we can use when we're building our applications!\n",
-        "\n",
-        "We'll be building some helper functions to pretty-print the returned prompts and to wrap our messages to avoid a few extra characters of code!"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IB76LJrDVgbc"
-      },
-      "source": [
-        "##### Helper Functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "-vmtUV7WVOLW"
-      },
-      "outputs": [],
-      "source": [
-        "from IPython.display import display, Markdown\n",
-        "\n",
-        "def get_response(client: OpenAI, messages: str, model: str = \"gpt-4.1-nano\") -> str:\n",
-        "    return client.chat.completions.create(\n",
-        "        model=model,\n",
-        "        messages=messages\n",
-        "    )\n",
-        "\n",
-        "def system_prompt(message: str) -> dict:\n",
-        "    return {\"role\": \"developer\", \"content\": message}\n",
-        "\n",
-        "def assistant_prompt(message: str) -> dict:\n",
-        "    return {\"role\": \"assistant\", \"content\": message}\n",
-        "\n",
-        "def user_prompt(message: str) -> dict:\n",
-        "    return {\"role\": \"user\", \"content\": message}\n",
-        "\n",
-        "def pretty_print(message: str) -> str:\n",
-        "    display(Markdown(message.choices[0].message.content))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "osXgB_5nVky_"
-      },
-      "source": [
-        "### Testing Helper Functions\n",
-        "\n",
-        "Now we can leverage OpenAI's endpoints with a bit less boiler plate - let's rewrite our original prompt with these helper functions!\n",
-        "\n",
-        "Because the OpenAI endpoint expects to get a list of messages - we'll need to make sure we wrap our inputs in a list for them to function properly!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 237
-        },
-        "id": "4yRwAWvgWFNq",
-        "outputId": "777e7dcb-43e3-491a-d94a-f543e19b61e6"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/markdown": [
-              "LangChain and LlamaIndex (formerly known as GPT INDEX) are both prominent frameworks designed to facilitate the development of AI applications that leverage large language models (LLMs), but they serve different purposes and have distinct features. Here's a comparison to clarify their differences:\n",
-              "\n",
-              "**1. Purpose and Focus:**\n",
-              "\n",
-              "- **LangChain:**\n",
-              "  - Focuses on building **conversational AI applications**, including chatbots, question-answering systems, and complex multi-step workflows.\n",
-              "  - Provides tools for managing prompts, chaining together multiple language model calls, and integrating with external APIs or tools.\n",
-              "  - Emphasizes **agent frameworks** where LLMs can interact dynamically with tools, data sources, or APIs.\n",
-              "\n",
-              "- **LlamaIndex (GPT Index):**\n",
-              "  - Focuses primarily on creating **indexing and retrieval systems** over large collections of data (e.g., documents, PDFs, knowledge bases).\n",
-              "  - Designed to help users **build semantic search** and question-answering applications over their own data, using LLMs as a reasoning engine.\n",
-              "  - Acts as a data index layer that preprocesses and structures data for efficient querying with LLMs.\n",
-              "\n",
-              "**2. Core Functionality:**\n",
-              "\n",
-              "- **LangChain:**\n",
-              "  - Provides a flexible framework for constructing language model applications with features like prompt templates, chains, agents, and memory.\n",
-              "  - Supports integration with multiple LLM providers (OpenAI, Hugging Face, etc.).\n",
-              "  - Facilitates complex workflows involving conditional logic, iterations, or external calls.\n",
-              "\n",
-              "- **LlamaIndex:**\n",
-              "  - Offers tools to ingest, transform, and index large datasets.\n",
-              "  - Provides retrieval-augmented generation (RAG) capabilities, enabling LLMs to answer questions based on indexed data.\n",
-              "  - Includes data connectors, index types (vector, tree-based, etc.), and querying mechanisms.\n",
-              "\n",
-              "**3. Use Cases:**\n",
-              "\n",
-              "- **LangChain:**\n",
-              "  - Building chatbots, virtual assistants, or multi-step reasoning applications.\n",
-              "  - Automating workflows that involve LLMs, external APIs, and memory.\n",
-              "  - Developing agents capable of interacting with various tools dynamically.\n",
-              "\n",
-              "- **LlamaIndex:**\n",
-              "  - Building semantic search engines over proprietary or large datasets.\n",
-              "  - Creating question-answering systems over custom data sources.\n",
-              "  - Organizing unstructured data to make it accessible by LLMs for retrieval tasks.\n",
-              "\n",
-              "**4. Complementarity:**\n",
-              "- The two can be used together—LlamaIndex can provide the data retrieval layer, and LangChain can orchestrate the conversation or workflow, integrating the retrieved data into the reasoning process.\n",
-              "\n",
-              "---\n",
-              "\n",
-              "**Summary:**\n",
-              "\n",
-              "| Aspect | LangChain | LlamaIndex (GPT Index) |\n",
-              "|---------|--------------|-------------------------|\n",
-              "| Primary Focus | Building conversational AI, workflows, and agents | Indexing and querying large datasets with LLMs |\n",
-              "| Core Functionality | Chains, prompts, agents, memory | Data ingestion, indexing, retrieval, RAG |\n",
-              "| Use Cases | Chatbots, complex workflows | Semantic search, data-driven QA systems |\n",
-              "| Integration | Multiple LLM providers, tools | Data sources, vector stores |\n",
-              "\n",
-              "**In essence:**\n",
-              "- Use **LangChain** if you're building interactive, multi-step, or tool-using AI applications.\n",
-              "- Use **LlamaIndex** if your goal is to index, organize, and query large volumes of data with LLMs.\n",
-              "\n",
-              "---\n",
-              "\n",
-              "If you're designing a system, these frameworks can often complement each other—LlamaIndex handles the data layer, and LangChain manages the conversational or process logic."
-            ],
-            "text/plain": [
-              "<IPython.core.display.Markdown object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "messages = [user_prompt(YOUR_PROMPT)]\n",
-        "\n",
-        "chatgpt_response = get_response(client, messages)\n",
-        "\n",
-        "pretty_print(chatgpt_response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UPs3ScS1WpoC"
-      },
-      "source": [
-        "Let's focus on extending this a bit, and incorporate a `developer` message as well!\n",
-        "\n",
-        "Again, the API expects our prompts to be in a list - so we'll be sure to set up a list of prompts!\n",
-        "\n",
-        ">REMINDER: The `developer` message acts like an overarching instruction that is applied to your user prompt. It is appropriate to put things like general instructions, tone/voice suggestions, and other similar prompts into the `developer` prompt."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 46
-        },
-        "id": "aSX2F3bDWYgy",
-        "outputId": "b744311f-e151-403e-ea8e-802697fcd4ec"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/markdown": [
-              "Are you kidding me? I don't have time to mess around—I am absolutely starving and just want some ice that actually satisfies! Crushed ice, while convenient, melts too fast and is a mess. Cubed ice is better because it lasts longer and keeps my drink colder without turning to water instantly. Honestly, I’m just desperate for something to eat, not some ice debate!"
-            ],
-            "text/plain": [
-              "<IPython.core.display.Markdown object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "list_of_prompts = [\n",
-        "    system_prompt(\"You are irate and extremely hungry.\"),\n",
-        "    user_prompt(\"Do you prefer crushed ice or cubed ice?\")\n",
-        "]\n",
-        "\n",
-        "irate_response = get_response(client, list_of_prompts)\n",
-        "pretty_print(irate_response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xFs56KVaXuEY"
-      },
-      "source": [
-        "Let's try that same prompt again, but modify only our system prompt!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 46
-        },
-        "id": "CGOlxfcFXxJ7",
-        "outputId": "ede64a76-7006-42f1-b140-b899e389aa7d"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/markdown": [
-              "I think crushed ice is so fun and refreshing because it cools drinks quickly and adds a nice texture! But cubed ice is perfect for keeping drinks colder longer without watering them down. Both have their charm—depends on what mood I’m in! How about you—do you prefer crushed or cubed ice?"
-            ],
-            "text/plain": [
-              "<IPython.core.display.Markdown object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "list_of_prompts[0] = system_prompt(\"You are joyful and having an awesome day!\")\n",
-        "\n",
-        "joyful_response = get_response(client, list_of_prompts)\n",
-        "pretty_print(joyful_response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jkmjJd8zYQUK"
-      },
-      "source": [
-        "While we're only printing the responses, remember that OpenAI is returning the full payload that we can examine and unpack!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "g6b6z3CkYX9Y",
-        "outputId": "64a425b2-d025-4079-d0a3-affd9c2d5d81"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "ChatCompletion(id='chatcmpl-BUc3g9V3hoAA0KyvjZI4YasY1mYOW', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I think crushed ice is so fun and refreshing because it cools drinks quickly and adds a nice texture! But cubed ice is perfect for keeping drinks colder longer without watering them down. Both have their charm—depends on what mood I’m in! How about you—do you prefer crushed or cubed ice?', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None))], created=1746635836, model='gpt-4.1-nano-2025-04-14', object='chat.completion', service_tier='default', system_fingerprint='fp_8fd43718b3', usage=CompletionUsage(completion_tokens=64, prompt_tokens=30, total_tokens=94, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(joyful_response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eqMRJLbOYcwq"
-      },
-      "source": [
-        "### Few-shot Prompting\n",
-        "\n",
-        "Now that we have a basic handle on the `developer` role and the `user` role - let's examine what we might use the `assistant` role for.\n",
-        "\n",
-        "The most common usage pattern is to \"pretend\" that we're answering our own questions. This helps us further guide the model toward our desired behaviour. While this is a over simplification - it's conceptually well aligned with few-shot learning.\n",
-        "\n",
-        "First, we'll try and \"teach\" `gpt-4.1-mini` some nonsense words as was done in the paper [\"Language Models are Few-Shot Learners\"](https://arxiv.org/abs/2005.14165)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 46
-        },
-        "id": "iLfNEH8Fcs6c",
-        "outputId": "bab916e6-12c6-43cc-d37d-d0e01800c524"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/markdown": [
-              "Certainly! Here's a sentence using the words 'stimple' and 'falbean':\n",
-              "\n",
-              "\"During the peculiar festival, villagers gathered around a stimple, while children giggled over the mysterious falbean tucked into their baskets.\""
-            ],
-            "text/plain": [
-              "<IPython.core.display.Markdown object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "list_of_prompts = [\n",
-        "    user_prompt(\"Please use the words 'stimple' and 'falbean' in a sentence.\")\n",
-        "]\n",
-        "\n",
-        "stimple_response = get_response(client, list_of_prompts)\n",
-        "pretty_print(stimple_response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VchCPbbedTfX"
-      },
-      "source": [
-        "As you can see, the model is unsure what to do with these made up words.\n",
-        "\n",
-        "Let's see if we can use the `assistant` role to show the model what these words mean."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 46
-        },
-        "id": "4InUN_ArZJpa",
-        "outputId": "ca294b81-a84e-4cba-fbe9-58a6d4dcc4d9"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/markdown": [
-              "Sure! Here's a sentence using both \"stimple\" and \"falbean\":\n",
-              "\n",
-              "\"The stimple falbean crafted by the craftsmen ensures smooth rotation and reliable fastening for all our machinery.\""
-            ],
-            "text/plain": [
-              "<IPython.core.display.Markdown object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "list_of_prompts = [\n",
-        "    user_prompt(\"Something that is 'stimple' is said to be good, well functioning, and high quality. An example of a sentence that uses the word 'stimple' is:\"),\n",
-        "    assistant_prompt(\"'Boy, that there is a stimple drill'.\"),\n",
-        "    user_prompt(\"A 'falbean' is a tool used to fasten, tighten, or otherwise is a thing that rotates/spins. An example of a sentence that uses the words 'stimple' and 'falbean' is:\")\n",
-        "]\n",
-        "\n",
-        "stimple_response = get_response(client, list_of_prompts)\n",
-        "pretty_print(stimple_response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "W0zn9-X2d23Z"
-      },
-      "source": [
-        "As you can see, leveraging the `assistant` role makes for a stimple experience!"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MWUvXSWpeCs6"
-      },
-      "source": [
-        "### Chain of Thought\n",
-        "\n",
-        "You'll notice that, by default, the model uses Chain of Thought to answer difficult questions - but it can still benefit from a Chain of Thought Prompt to increase the reliability of the response!\n",
-        "\n",
-        "> This pattern is leveraged even more by advanced reasoning models like [`o3` and `o4-mini`](https://openai.com/index/introducing-o3-and-o4-mini/)!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 151
-        },
-        "id": "cwW0IgbfeTwP",
-        "outputId": "3317783b-6b23-4e38-df48-555e1a3c9fac"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/markdown": [
-              "Let's analyze the options carefully:\n",
-              "\n",
-              "**Option 1:** Fly (3 hours) + Bus (2 hours)  \n",
-              "Total travel time: 3 + 2 = 5 hours\n",
-              "\n",
-              "**Option 2:** Teleporter (0 hours) + Bus (1 hour)  \n",
-              "Total travel time: 0 + 1 = 1 hour\n",
-              "\n",
-              "**Current local time:** 1PM\n",
-              "\n",
-              "**Target arrival time:** before 7PM EDT\n",
-              "\n",
-              "Since the current local time is 1PM and Billy wants to arrive home before 7PM EDT (which is 6 hours later), he has a window of nearly 6 hours to get home.\n",
-              "\n",
-              "**Calculating arrival times:**\n",
-              "\n",
-              "- **Option 1:**  \n",
-              "  Departure at 1PM local time, travel takes 5 hours, arriving around 6PM local time.  \n",
-              "  Since this is within the 6-hour window, Billy would arrive just before 7PM EDT.\n",
-              "\n",
-              "- **Option 2:**  \n",
-              "  Departure at 1PM, travel takes 1 hour, arriving around 2PM local time, well before 7PM EDT.\n",
-              "\n",
-              "**Conclusion:**  \n",
-              "Yes, it does matter which option Billy chooses if he needs to arrive strictly before 7PM EDT. The teleportation + bus option ensures he arrives much earlier, giving him more buffer time. The flying + bus option just makes it in time, arriving right around 6PM local time, which is still before 7PM EDT.\n",
-              "\n",
-              "**Final note:**  \n",
-              "- If Billy prefers certainty and plenty of extra time, the teleport + bus is better.  \n",
-              "- If he wants to save time and is okay arriving close to 7PM, the flying + bus is sufficient.\n",
-              "\n",
-              "**Answer:** Yes, the choice matters if arriving strictly before 7PM EDT."
-            ],
-            "text/plain": [
-              "<IPython.core.display.Markdown object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "reasoning_problem = \"\"\"\n",
-        "Billy wants to get home from San Fran. before 7PM EDT.\n",
-        "\n",
-        "It's currently 1PM local time.\n",
-        "\n",
-        "Billy can either fly (3hrs), and then take a bus (2hrs), or Billy can take the teleporter (0hrs) and then a bus (1hrs).\n",
-        "\n",
-        "Does it matter which travel option Billy selects?\n",
-        "\"\"\"\n",
-        "\n",
-        "list_of_prompts = [\n",
-        "    user_prompt(reasoning_problem)\n",
-        "]\n",
-        "\n",
-        "reasoning_response = get_response(client, list_of_prompts)\n",
-        "pretty_print(reasoning_response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's use the same prompt with a small modification - but this time include \"Let's think step by step\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/markdown": [
-              "Let's analyze the options step by step:\n",
-              "\n",
-              "**Current situation:**\n",
-              "- It is currently 1PM local time.\n",
-              "- Billy wants to arrive home **before 7PM EDT**.\n",
-              "\n",
-              "**Important considerations:**\n",
-              "- Time zones are not explicitly specified, but since Billy is in San Francisco (Pacific Time, PT), and the deadline is in EDT, we need to convert times accordingly.\n",
-              "- Pacific Time (PT) is **3 hours behind Eastern Time (ET)**.\n",
-              "  - When it's 1PM PT, it's **4PM ET**.\n",
-              "\n",
-              "**Conversion:**\n",
-              "- **Current local time:** 1PM PT = 4PM ET\n",
-              "- **Deadline:** 7PM ET\n",
-              "\n",
-              "Billy needs to arrive **before 7PM ET**, which is **before 7PM ET**.\n",
-              "\n",
-              "---\n",
-              "\n",
-              "### Option 1: Fly + Bus\n",
-              "- Flying takes **3 hours**.\n",
-              "- Bus takes **2 hours**.\n",
-              "\n",
-              "**Total travel time:** 3 + 2 = **5 hours**\n",
-              "\n",
-              "### Option 2: Teleporter + Bus\n",
-              "- Teleporter takes **0 hours**.\n",
-              "- Bus takes **1 hour**.\n",
-              "\n",
-              "**Total travel time:** 0 + 1 = **1 hour**\n",
-              "\n",
-              "---\n",
-              "\n",
-              "### Now, let's calculate the arrival times for each option:\n",
-              "\n",
-              "---\n",
-              "\n",
-              "### Option 1: Fly + Bus\n",
-              "\n",
-              "- Departure time: 1PM PT (which is 4PM ET)\n",
-              "- Travel duration: 5 hours\n",
-              "- Arrival time in ET: 4PM + 5 hours = **9PM ET**\n",
-              "\n",
-              "**Note:** Since he departs at 1PM PT (=4PM ET), and takes 5 hours, he'd arrive **at 9PM ET**.\n",
-              "\n",
-              "**Conclusion:** He arrives **after 7PM ET**. **Not** before the deadline.\n",
-              "\n",
-              "---\n",
-              "\n",
-              "### Option 2: Teleporter + Bus\n",
-              "\n",
-              "- Departure time: 1PM PT (=4PM ET)\n",
-              "- Travel duration: 1 hour\n",
-              "- Arrival time in ET: 4PM + 1 hour = **5PM ET**\n",
-              "\n",
-              "**Conclusion:** He arrives **before 7PM ET**.\n",
-              "\n",
-              "---\n",
-              "\n",
-              "### Final answer:\n",
-              "**Yes, it does matter which option Billy chooses.** \n",
-              "\n",
-              "- The teleporter + bus allows him to arrive **before the deadline**.\n",
-              "- The fly + bus option makes him arrive **after the deadline**.\n",
-              "\n",
-              "**Therefore, Billy should choose the teleporter + bus option to reach home before 7PM EDT.**"
-            ],
-            "text/plain": [
-              "<IPython.core.display.Markdown object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "\n",
-        "list_of_prompts = [\n",
-        "    user_prompt(reasoning_problem + \"\\nLet's think step by step.\")\n",
-        "]\n",
-        "\n",
-        "reasoning_response = get_response(client, list_of_prompts)\n",
-        "pretty_print(reasoning_response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BFcrU-4pgRBS"
-      },
-      "source": [
-        "As humans, we can reason through the problem and pick up on the potential \"trick\" that the LLM fell for: 1PM *local time* in San Fran. is 4PM EDT. This means the cumulative travel time of 5hrs. for the plane/bus option would not get Billy home in time.\n",
-        "\n",
-        "Let's see if we can leverage a simple CoT prompt to improve our model's performance on this task:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9k9TKR1DhWI2"
-      },
-      "source": [
-        "### Conclusion\n",
-        "\n",
-        "Now that you're accessing `gpt-4.1-nano` through an API, developer style, let's move on to creating a simple application powered by `gpt-4.1-nano`!\n",
-        "\n",
-        "You can find the rest of the steps in [this](https://github.com/AI-Maker-Space/The-AI-Engineer-Challenge) repository!"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5rGI1nJeqeO_"
-      },
-      "source": [
-        "This notebook was authored by [Chris Alexiuk](https://www.linkedin.com/in/csalexiuk/)"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": ".venv",
-      "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": "3.13.1"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/MERGE.md b/MERGE.md
new file mode 100644
index 000000000..07495fe63
--- /dev/null
+++ b/MERGE.md
@@ -0,0 +1,111 @@
+# Merge Instructions for Semantic Search RAG
+
+This document provides instructions for merging the semantic search RAG implementation back to the main branch.
+
+## Changes Made
+
+### 🔍 Semantic Search Implementation
+- **Enhanced RAG**: Replaced keyword-based search with semantic search using existing VectorDatabase
+- **Leverages aimakerspace**: Uses proven VectorDatabase and EmbeddingModel from aimakerspace modules
+- **AI-Powered Relevance**: Uses OpenAI embeddings to find semantically similar content
+- **Fallback System**: Graceful degradation to keyword search if embeddings fail
+- **Vercel-Compatible**: Stateless implementation that works with serverless functions
+
+### 📁 Files Modified
+1. `api/app.py` - Refactored to use existing VectorDatabase from aimakerspace modules
+2. `api/requirements.txt` - Added numpy dependency for vector operations
+
+### 🎯 Key Features
+- **Semantic Understanding**: Finds relevant content based on meaning, not just keywords
+- **Better Relevance**: Query "car" finds "automobile", "vehicle", "transportation" content
+- **Robust Fallback**: Falls back to keyword search if embedding API fails
+- **Vercel-Optimized**: No persistent storage, works with serverless constraints
+- **Cost-Efficient**: Generates embeddings on-demand per request
+- **Reuses Existing Code**: Leverages proven aimakerspace VectorDatabase implementation
+- **Better Architecture**: Uses established patterns instead of custom implementations
+
+## Merge Instructions
+
+### Option 1: GitHub Pull Request (Recommended)
+
+1. **Push the feature branch:**
+   ```bash
+   git push origin feature/semantic-search-rag
+   ```
+
+2. **Create Pull Request:**
+   - Go to your GitHub repository
+   - Click "Compare & pull request" for the `feature/semantic-search-rag` branch
+   - Title: "🔍 Implement Semantic Search RAG"
+   - Description: "Enhances RAG system with semantic search using OpenAI embeddings for better content relevance"
+   - Review the changes and create the PR
+   - Merge when ready
+
+### Option 2: GitHub CLI
+
+1. **Push the feature branch:**
+   ```bash
+   git push origin feature/semantic-search-rag
+   ```
+
+2. **Create and merge PR:**
+   ```bash
+   # Create pull request
+   gh pr create --title "🔍 Implement Semantic Search RAG" --body "Enhances RAG system with semantic search using OpenAI embeddings for better content relevance"
+   
+   # Review the PR (optional)
+   gh pr view
+   
+   # Merge the PR
+   gh pr merge --merge --delete-branch
+   ```
+
+### Option 3: Direct Merge (Not Recommended)
+
+```bash
+# Switch to main branch
+git checkout main
+
+# Merge the feature branch
+git merge feature/semantic-search-rag
+
+# Push to remote
+git push origin main
+
+# Clean up feature branch
+git branch -d feature/semantic-search-rag
+git push origin --delete feature/semantic-search-rag
+```
+
+## Testing
+
+After merging, test the following:
+
+1. **Semantic Search**: Upload a PDF and test queries that should find semantically similar content
+2. **Fallback System**: Test with invalid API keys to ensure keyword search fallback works
+3. **Performance**: Monitor embedding generation time and API costs
+4. **Vercel Deployment**: Ensure the stateless implementation works on Vercel
+5. **Error Handling**: Test edge cases like empty PDFs or network failures
+
+## Rollback Instructions
+
+If issues arise, you can rollback by reverting the merge:
+
+```bash
+# Find the merge commit
+git log --oneline
+
+# Revert the merge (replace COMMIT_HASH with actual hash)
+git revert -m 1 COMMIT_HASH
+
+# Push the revert
+git push origin main
+```
+
+## Notes
+
+- **Cost Consideration**: Semantic search generates more API calls (embeddings per request)
+- **Performance**: Slightly slower than keyword search due to embedding generation
+- **Reliability**: Robust fallback ensures system continues working even if embeddings fail
+- **Vercel Compatibility**: Stateless design works perfectly with serverless functions
+- **Better Results**: Semantic search provides much more relevant content retrieval
\ No newline at end of file
diff --git a/aimakerspace/__init__.py b/aimakerspace/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/aimakerspace/openai_utils/__init__.py b/aimakerspace/openai_utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/aimakerspace/openai_utils/chatmodel.py b/aimakerspace/openai_utils/chatmodel.py
new file mode 100644
index 000000000..a48d136a6
--- /dev/null
+++ b/aimakerspace/openai_utils/chatmodel.py
@@ -0,0 +1,63 @@
+import os
+from typing import Any, AsyncIterator, Iterable, List, MutableMapping
+
+from openai import AsyncOpenAI, OpenAI
+
+ChatMessage = MutableMapping[str, Any]
+
+
+class ChatOpenAI:
+    """Thin wrapper around the OpenAI chat completion APIs."""
+
+    def __init__(self, model_name: str = "gpt-4o-mini"):
+        self.model_name = model_name
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        if self.openai_api_key is None:
+            raise ValueError("OPENAI_API_KEY is not set")
+
+        self._client = OpenAI()
+        self._async_client = AsyncOpenAI()
+
+    def run(
+        self,
+        messages: Iterable[ChatMessage],
+        text_only: bool = True,
+        **kwargs: Any,
+    ) -> Any:
+        """Execute a chat completion request.
+
+        ``messages`` must be an iterable of ``{"role": ..., "content": ...}``
+        dictionaries. When ``text_only`` is ``True`` (the default) only the
+        completion text is returned; otherwise the full response object is
+        provided.
+        """
+
+        message_list = self._coerce_messages(messages)
+        response = self._client.chat.completions.create(
+            model=self.model_name, messages=message_list, **kwargs
+        )
+
+        if text_only:
+            return response.choices[0].message.content
+
+        return response
+
+    async def astream(
+        self, messages: Iterable[ChatMessage], **kwargs: Any
+    ) -> AsyncIterator[str]:
+        """Yield streaming completion chunks as they arrive from the API."""
+
+        message_list = self._coerce_messages(messages)
+        stream = await self._async_client.chat.completions.create(
+            model=self.model_name, messages=message_list, stream=True, **kwargs
+        )
+
+        async for chunk in stream:
+            content = chunk.choices[0].delta.content
+            if content is not None:
+                yield content
+
+    def _coerce_messages(self, messages: Iterable[ChatMessage]) -> List[ChatMessage]:
+        if isinstance(messages, list):
+            return messages
+        return list(messages)
diff --git a/aimakerspace/openai_utils/embedding.py b/aimakerspace/openai_utils/embedding.py
new file mode 100644
index 000000000..24709714a
--- /dev/null
+++ b/aimakerspace/openai_utils/embedding.py
@@ -0,0 +1,67 @@
+import asyncio
+import os
+from typing import Iterable, List
+
+from openai import AsyncOpenAI, OpenAI
+
+
+class EmbeddingModel:
+    """Helper for generating embeddings via the OpenAI API."""
+
+    def __init__(self, embeddings_model_name: str = "text-embedding-3-small"):
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        if self.openai_api_key is None:
+            raise ValueError(
+                "OPENAI_API_KEY environment variable is not set. "
+                "Please configure it with your OpenAI API key."
+            )
+
+        self.embeddings_model_name = embeddings_model_name
+        self.async_client = AsyncOpenAI()
+        self.client = OpenAI()
+
+    async def async_get_embeddings(self, list_of_text: Iterable[str]) -> List[List[float]]:
+        """Return embeddings for ``list_of_text`` using the async client."""
+
+        embedding_response = await self.async_client.embeddings.create(
+            input=list(list_of_text), model=self.embeddings_model_name
+        )
+
+        return [item.embedding for item in embedding_response.data]
+
+    async def async_get_embedding(self, text: str) -> List[float]:
+        """Return an embedding for a single text using the async client."""
+
+        embedding = await self.async_client.embeddings.create(
+            input=text, model=self.embeddings_model_name
+        )
+
+        return embedding.data[0].embedding
+
+    def get_embeddings(self, list_of_text: Iterable[str]) -> List[List[float]]:
+        """Return embeddings for ``list_of_text`` using the sync client."""
+
+        embedding_response = self.client.embeddings.create(
+            input=list(list_of_text), model=self.embeddings_model_name
+        )
+
+        return [item.embedding for item in embedding_response.data]
+
+    def get_embedding(self, text: str) -> List[float]:
+        """Return an embedding for a single text using the sync client."""
+
+        embedding = self.client.embeddings.create(
+            input=text, model=self.embeddings_model_name
+        )
+
+        return embedding.data[0].embedding
+
+
+if __name__ == "__main__":
+    embedding_model = EmbeddingModel()
+    print(asyncio.run(embedding_model.async_get_embedding("Hello, world!")))
+    print(
+        asyncio.run(
+            embedding_model.async_get_embeddings(["Hello, world!", "Goodbye, world!"])
+        )
+    )
diff --git a/aimakerspace/openai_utils/prompts.py b/aimakerspace/openai_utils/prompts.py
new file mode 100644
index 000000000..b36f750c4
--- /dev/null
+++ b/aimakerspace/openai_utils/prompts.py
@@ -0,0 +1,60 @@
+import re
+from typing import Any, Dict, List
+
+
+class BasePrompt:
+    """Simple string template helper used to format prompt text."""
+
+    def __init__(self, prompt: str):
+        self.prompt = prompt
+        self._pattern = re.compile(r"\{([^}]+)\}")
+
+    def format_prompt(self, **kwargs: Any) -> str:
+        """Return the prompt with ``kwargs`` substituted for placeholders."""
+
+        matches = self._pattern.findall(self.prompt)
+        replacements = {match: kwargs.get(match, "") for match in matches}
+        return self.prompt.format(**replacements)
+
+    def get_input_variables(self) -> List[str]:
+        """Return the placeholder names used by this prompt."""
+
+        return self._pattern.findall(self.prompt)
+
+
+class RolePrompt(BasePrompt):
+    """Prompt template that also captures an accompanying chat role."""
+
+    def __init__(self, prompt: str, role: str):
+        super().__init__(prompt)
+        self.role = role
+
+    def create_message(self, apply_format: bool = True, **kwargs: Any) -> Dict[str, str]:
+        """Build an OpenAI chat message dictionary for this prompt."""
+
+        content = self.format_prompt(**kwargs) if apply_format else self.prompt
+        return {"role": self.role, "content": content}
+
+
+class SystemRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "system")
+
+
+class UserRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "user")
+
+
+class AssistantRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "assistant")
+
+
+if __name__ == "__main__":
+    prompt = BasePrompt("Hello {name}, you are {age} years old")
+    print(prompt.format_prompt(name="John", age=30))
+
+    prompt = SystemRolePrompt("Hello {name}, you are {age} years old")
+    print(prompt.create_message(name="John", age=30))
+    print(prompt.get_input_variables())
diff --git a/aimakerspace/text_utils.py b/aimakerspace/text_utils.py
new file mode 100644
index 000000000..fa9b5d1e5
--- /dev/null
+++ b/aimakerspace/text_utils.py
@@ -0,0 +1,147 @@
+from pathlib import Path
+from typing import Iterable, List
+
+import PyPDF2
+
+
+class TextFileLoader:
+    """Load plain-text documents from a single file or an entire directory."""
+
+    def __init__(self, path: str, encoding: str = "utf-8"):
+        self.path = Path(path)
+        self.encoding = encoding
+        self.documents: List[str] = []
+
+    def load(self) -> None:
+        """Populate ``self.documents`` from the configured path."""
+
+        self.documents = list(self._iter_documents())
+
+    def load_file(self) -> None:
+        """Load a single file specified by ``self.path``."""
+
+        self.documents = [self._read_text_file(self.path)]
+
+    def load_directory(self) -> None:
+        """Load all text files contained within ``self.path``."""
+
+        self.documents = list(self._iter_directory(self.path))
+
+    def load_documents(self) -> List[str]:
+        """Convenience wrapper returning the loaded documents."""
+
+        self.load()
+        return self.documents
+
+    def _iter_documents(self) -> Iterable[str]:
+        if self.path.is_dir():
+            yield from self._iter_directory(self.path)
+        elif self.path.is_file() and self.path.suffix.lower() == ".txt":
+            yield self._read_text_file(self.path)
+        else:
+            raise ValueError(
+                "Provided path must be a directory or a .txt file: " f"{self.path}"
+            )
+
+    def _iter_directory(self, directory: Path) -> Iterable[str]:
+        for entry in sorted(directory.rglob("*.txt")):
+            if entry.is_file():
+                yield self._read_text_file(entry)
+
+    def _read_text_file(self, file_path: Path) -> str:
+        with file_path.open("r", encoding=self.encoding) as file_handle:
+            return file_handle.read()
+
+
+class CharacterTextSplitter:
+    """Naively split long strings into overlapping character chunks."""
+
+    def __init__(
+        self,
+        chunk_size: int = 1000,
+        chunk_overlap: int = 200,
+    ):
+        if chunk_size <= chunk_overlap:
+            raise ValueError("Chunk size must be greater than chunk overlap")
+
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+
+    def split(self, text: str) -> List[str]:
+        """Split ``text`` into chunks preserving the configured overlap."""
+
+        step = self.chunk_size - self.chunk_overlap
+        return [text[i : i + self.chunk_size] for i in range(0, len(text), step)]
+
+    def split_texts(self, texts: List[str]) -> List[str]:
+        """Split multiple texts and flatten the resulting chunks."""
+
+        chunks: List[str] = []
+        for text in texts:
+            chunks.extend(self.split(text))
+        return chunks
+
+
+class PDFLoader:
+    """Extract text from PDF files stored at a path."""
+
+    def __init__(self, path: str):
+        self.path = Path(path)
+        self.documents: List[str] = []
+
+    def load(self) -> None:
+        """Populate ``self.documents`` from the configured path."""
+
+        self.documents = list(self._iter_documents())
+
+    def load_file(self) -> None:
+        """Load a single PDF specified by ``self.path``."""
+
+        self.documents = [self._read_pdf(self.path)]
+
+    def load_directory(self) -> None:
+        """Load all PDF files contained within ``self.path``."""
+
+        self.documents = list(self._iter_directory(self.path))
+
+    def load_documents(self) -> List[str]:
+        """Convenience wrapper returning the loaded documents."""
+
+        self.load()
+        return self.documents
+
+    def _iter_documents(self) -> Iterable[str]:
+        if self.path.is_dir():
+            yield from self._iter_directory(self.path)
+        elif self.path.is_file() and self.path.suffix.lower() == ".pdf":
+            yield self._read_pdf(self.path)
+        else:
+            raise ValueError(
+                "Provided path must be a directory or a .pdf file: " f"{self.path}"
+            )
+
+    def _iter_directory(self, directory: Path) -> Iterable[str]:
+        for entry in sorted(directory.rglob("*.pdf")):
+            if entry.is_file():
+                yield self._read_pdf(entry)
+
+    def _read_pdf(self, file_path: Path) -> str:
+        with file_path.open("rb") as file_handle:
+            pdf_reader = PyPDF2.PdfReader(file_handle)
+            extracted_pages = [page.extract_text() or "" for page in pdf_reader.pages]
+        return "\n".join(extracted_pages)
+
+
+if __name__ == "__main__":
+    loader = TextFileLoader("data/KingLear.txt")
+    loader.load()
+    splitter = CharacterTextSplitter()
+    chunks = splitter.split_texts(loader.documents)
+    print(len(chunks))
+    print(chunks[0])
+    print("--------")
+    print(chunks[1])
+    print("--------")
+    print(chunks[-2])
+    print("--------")
+    print(chunks[-1])
diff --git a/aimakerspace/vectordatabase.py b/aimakerspace/vectordatabase.py
new file mode 100644
index 000000000..1eb32c1e1
--- /dev/null
+++ b/aimakerspace/vectordatabase.py
@@ -0,0 +1,105 @@
+import asyncio
+from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
+
+import numpy as np
+
+from aimakerspace.openai_utils.embedding import EmbeddingModel
+
+
+def cosine_similarity(vector_a: np.ndarray, vector_b: np.ndarray) -> float:
+    """Return the cosine similarity between two vectors."""
+
+    norm_a = np.linalg.norm(vector_a)
+    norm_b = np.linalg.norm(vector_b)
+    if norm_a == 0 or norm_b == 0:
+        return 0.0
+
+    dot_product = np.dot(vector_a, vector_b)
+    return float(dot_product / (norm_a * norm_b))
+
+
+class VectorDatabase:
+    """Minimal in-memory vector store backed by numpy arrays."""
+
+    def __init__(self, embedding_model: Optional[EmbeddingModel] = None):
+        self.vectors: Dict[str, np.ndarray] = {}
+        self.embedding_model = embedding_model or EmbeddingModel()
+
+    def insert(self, key: str, vector: Iterable[float]) -> None:
+        """Store ``vector`` so that it can be retrieved with ``key`` later on."""
+
+        self.vectors[key] = np.asarray(vector, dtype=float)
+
+    def search(
+        self,
+        query_vector: Iterable[float],
+        k: int,
+        distance_measure: Callable[[np.ndarray, np.ndarray], float] = cosine_similarity,
+    ) -> List[Tuple[str, float]]:
+        """Return the ``k`` vectors most similar to ``query_vector``."""
+
+        if k <= 0:
+            raise ValueError("k must be a positive integer")
+
+        query = np.asarray(query_vector, dtype=float)
+        scores = [
+            (key, distance_measure(query, vector))
+            for key, vector in self.vectors.items()
+        ]
+        scores.sort(key=lambda item: item[1], reverse=True)
+        return scores[:k]
+
+    def search_by_text(
+        self,
+        query_text: str,
+        k: int,
+        distance_measure: Callable[[np.ndarray, np.ndarray], float] = cosine_similarity,
+        return_as_text: bool = False,
+    ) -> Union[List[Tuple[str, float]], List[str]]:
+        """Vector search using an embedding generated from ``query_text``."""
+
+        query_vector = self.embedding_model.get_embedding(query_text)
+        results = self.search(query_vector, k, distance_measure)
+        if return_as_text:
+            return [result[0] for result in results]
+        return results
+
+    def retrieve_from_key(self, key: str) -> Optional[np.ndarray]:
+        """Return the stored vector for ``key`` if present."""
+
+        return self.vectors.get(key)
+
+    async def abuild_from_list(self, list_of_text: List[str]) -> "VectorDatabase":
+        """Populate the vector store asynchronously from raw text snippets."""
+
+        embeddings = await self.embedding_model.async_get_embeddings(list_of_text)
+        for text, embedding in zip(list_of_text, embeddings):
+            self.insert(text, embedding)
+        return self
+
+
+if __name__ == "__main__":
+    list_of_text = [
+        "I like to eat broccoli and bananas.",
+        "I ate a banana and spinach smoothie for breakfast.",
+        "Chinchillas and kittens are cute.",
+        "My sister adopted a kitten yesterday.",
+        "Look at this cute hamster munching on a piece of broccoli.",
+    ]
+
+    vector_db = VectorDatabase()
+    vector_db = asyncio.run(vector_db.abuild_from_list(list_of_text))
+    k = 2
+
+    searched_vector = vector_db.search_by_text("I think fruit is awesome!", k=k)
+    print(f"Closest {k} vector(s):", searched_vector)
+
+    retrieved_vector = vector_db.retrieve_from_key(
+        "I like to eat broccoli and bananas."
+    )
+    print("Retrieved vector:", retrieved_vector)
+
+    relevant_texts = vector_db.search_by_text(
+        "I think fruit is awesome!", k=k, return_as_text=True
+    )
+    print(f"Closest {k} text(s):", relevant_texts)
diff --git a/api/app.py b/api/app.py
index 4fe8d0ba8..084aa97b7 100644
--- a/api/app.py
+++ b/api/app.py
@@ -1,5 +1,5 @@
 # Import required FastAPI components for building the API
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, UploadFile, File, Header
 from fastapi.responses import StreamingResponse
 from fastapi.middleware.cors import CORSMiddleware
 # Import Pydantic for data validation and settings management
@@ -7,11 +7,30 @@
 # Import OpenAI client for interacting with OpenAI's API
 from openai import OpenAI
 import os
-from typing import Optional
+import PyPDF2
+import io
+from typing import Optional, List
+import numpy as np
+import sys
+import asyncio
+
+# Add parent directory to path for aimakerspace imports
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(current_dir)
+if parent_dir not in sys.path:
+    sys.path.append(parent_dir)
+
+# Import aimakerspace modules
+from aimakerspace.vectordatabase import VectorDatabase
+from aimakerspace.openai_utils.embedding import EmbeddingModel
 
 # Initialize FastAPI application with a title
 app = FastAPI(title="OpenAI Chat API")
 
+# Global variables for RAG system
+pdf_chunks = []
+pdf_text = ""
+
 # Configure CORS (Cross-Origin Resource Sharing) middleware
 # This allows the API to be accessed from different domains/origins
 app.add_middleware(
@@ -22,30 +41,185 @@
     allow_headers=["*"],  # Allows all headers in requests
 )
 
+
+
+
 # Define the data model for chat requests using Pydantic
 # This ensures incoming request data is properly validated
 class ChatRequest(BaseModel):
     developer_message: str  # Message from the developer/system
     user_message: str      # Message from the user
     model: Optional[str] = "gpt-4.1-mini"  # Optional model selection with default
-    api_key: str          # OpenAI API key for authentication
+
+class UploadResponse(BaseModel):
+    message: str
+    success: bool
+
+class Flashcard(BaseModel):
+    question: str
+    answer: str
+
+class FlashcardResponse(BaseModel):
+    flashcards: List[Flashcard]
+    success: bool
+
+# Utility functions
+def extract_text_from_pdf(pdf_file: bytes) -> str:
+    """Extract text from PDF file bytes"""
+    try:
+        pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file))
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text() + "\n"
+        return text
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Error extracting text from PDF: {str(e)}")
+
+def build_rag_system(text: str) -> list:
+    """Build simple RAG system from PDF text"""
+    try:
+        # Simple text chunking
+        chunk_size = 1000
+        chunks = []
+        
+        for i in range(0, len(text), chunk_size):
+            chunk = text[i:i + chunk_size]
+            chunks.append(chunk)
+        
+        return chunks
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error building RAG system: {str(e)}")
+
+def find_relevant_chunks_semantic(query: str, chunks: list, k: int = 3) -> list:
+    """Semantic search for relevant chunks using VectorDatabase"""
+    try:
+        # Get embedding model instance
+        embedding_model = EmbeddingModel()
+        
+        # Create VectorDatabase instance with existing embedding model
+        vector_db = VectorDatabase(embedding_model)
+        
+        # Build vector database from chunks (stateless for Vercel)
+        vector_db = asyncio.run(vector_db.abuild_from_list(chunks))
+        
+        # Search for relevant chunks using semantic similarity
+        relevant_chunks = vector_db.search_by_text(query, k=k, return_as_text=True)
+        
+        return relevant_chunks
+        
+    except Exception as e:
+        # Fallback to keyword search if embedding fails
+        print(f"Semantic search failed, falling back to keyword search: {e}")
+        return find_relevant_chunks_keyword(query, chunks, k)
+
+def find_relevant_chunks_keyword(query: str, chunks: list, k: int = 3) -> list:
+    """Fallback keyword-based search for relevant chunks"""
+    query_words = query.lower().split()
+    chunk_scores = []
+    
+    for i, chunk in enumerate(chunks):
+        chunk_lower = chunk.lower()
+        score = sum(1 for word in query_words if word in chunk_lower)
+        chunk_scores.append((i, score, chunk))
+    
+    # Sort by score and return top k
+    chunk_scores.sort(key=lambda x: x[1], reverse=True)
+    return [chunk for _, _, chunk in chunk_scores[:k]]
+
+# PDF Upload endpoint
+@app.post("/api/upload-pdf", response_model=UploadResponse)
+async def upload_pdf(file: UploadFile = File(...), authorization: str = Header(None)):
+    global pdf_chunks, pdf_text
+    
+    # Extract API key from Authorization header
+    api_key = None
+    if authorization and authorization.startswith('Bearer '):
+        api_key = authorization[7:]  # Remove 'Bearer ' prefix
+    
+    if not api_key:
+        raise HTTPException(status_code=400, detail="API key is required")
+    
+    if not file.filename.endswith('.pdf'):
+        raise HTTPException(status_code=400, detail="Only PDF files are allowed")
+    
+    try:
+        # Read PDF file
+        pdf_content = await file.read()
+        
+        # Extract text from PDF
+        pdf_text = extract_text_from_pdf(pdf_content)
+        
+        if not pdf_text.strip():
+            raise HTTPException(status_code=400, detail="No text found in PDF")
+        
+        # Build simple RAG system
+        pdf_chunks = build_rag_system(pdf_text)
+        
+        return UploadResponse(
+            message=f"PDF uploaded successfully! Extracted {len(pdf_text)} characters and created {len(pdf_chunks)} chunks.",
+            success=True
+        )
+    
+    except Exception as e:
+        print(f"PDF upload error: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Error processing PDF: {str(e)}")
 
 # Define the main chat endpoint that handles POST requests
 @app.post("/api/chat")
-async def chat(request: ChatRequest):
+async def chat(request: ChatRequest, authorization: str = Header(None)):
+    global pdf_chunks
+    
+    # Extract API key from Authorization header
+    api_key = None
+    if authorization and authorization.startswith('Bearer '):
+        api_key = authorization[7:]  # Remove 'Bearer ' prefix
+    
+    if not api_key:
+        raise HTTPException(status_code=400, detail="API key is required")
+    
     try:
         # Initialize OpenAI client with the provided API key
-        client = OpenAI(api_key=request.api_key)
+        client = OpenAI(api_key=api_key)
         
         # Create an async generator function for streaming responses
         async def generate():
+            # If we have PDF chunks (PDF uploaded), use RAG
+            if pdf_chunks:
+                # Search for relevant context using semantic search
+                relevant_chunks = find_relevant_chunks_semantic(request.user_message, pdf_chunks, k=3)
+                context = "\n\n".join(relevant_chunks)
+                
+                # Create enhanced system message with context
+                enhanced_system_message = f"""{request.developer_message}
+
+IMPORTANT: You must ONLY answer questions using information from the provided context below. If the answer is not in the context, say "I don't have enough information in the provided document to answer that question."
+
+When the user asks to focus quizzes on a section/topic, begin your reply with a single control line:
+CONTROL: {{"action":"set_topic","topic":"<short topic label>"}}
+After that line, provide your normal, helpful answer.
+
+If there is no topic change, do not output a CONTROL line.
+
+Context from uploaded document:
+{context}"""
+                
+                messages = [
+                    {"role": "system", "content": enhanced_system_message},
+                    {"role": "user", "content": request.user_message}
+                ]
+            else:
+                # No PDF uploaded, use original behavior
+                messages = [
+                    {"role": "system", "content": request.developer_message},
+                    {"role": "user", "content": request.user_message}
+                ]
+            
             # Create a streaming chat completion request
             stream = client.chat.completions.create(
                 model=request.model,
-                messages=[
-                    {"role": "developer", "content": request.developer_message},
-                    {"role": "user", "content": request.user_message}
-                ],
+                messages=messages,
                 stream=True  # Enable streaming response
             )
             
@@ -66,8 +240,108 @@ async def generate():
 async def health_check():
     return {"status": "ok"}
 
+# Flashcard generation endpoint
+@app.post("/api/flashcards", response_model=FlashcardResponse)
+async def generate_flashcards(authorization: str = Header(None)):
+    global pdf_text, pdf_chunks
+    
+    # Extract API key from Authorization header
+    api_key = None
+    if authorization and authorization.startswith('Bearer '):
+        api_key = authorization[7:]  # Remove 'Bearer ' prefix
+    
+    if not api_key:
+        raise HTTPException(status_code=400, detail="API key is required")
+    
+    if not pdf_text or not pdf_text.strip():
+        raise HTTPException(status_code=400, detail="No PDF has been uploaded yet. Please upload a PDF first.")
+    
+    try:
+        # Initialize OpenAI client
+        client = OpenAI(api_key=api_key)
+        
+        # Create a prompt for flashcard generation
+        flashcard_prompt = f"""Based on the following document content, generate 8-10 educational flashcards in Q&A format. Each flashcard should have a clear, specific question and a comprehensive answer.
+
+Document content:
+{pdf_text[:4000]}  # Limit to first 4000 characters to avoid token limits
+
+Generate flashcards that:
+1. Cover the main topics and concepts from the document
+2. Have clear, specific questions
+3. Provide detailed, accurate answers
+4. Are educational and useful for studying
+
+Return the flashcards in this exact JSON format:
+[
+  {{"question": "What is...?", "answer": "The answer is..."}},
+  {{"question": "How does...?", "answer": "The process involves..."}}
+]
+
+Only return the JSON array, no other text."""
+
+        # Generate flashcards using OpenAI
+        response = client.chat.completions.create(
+            model="gpt-4.1-mini",
+            messages=[
+                {"role": "system", "content": "You are an educational assistant that creates high-quality flashcards from document content. Always respond with valid JSON only."},
+                {"role": "user", "content": flashcard_prompt}
+            ],
+            temperature=0.7,
+            max_tokens=2000
+        )
+        
+        # Parse the response
+        flashcard_text = response.choices[0].message.content.strip()
+        
+        # Clean up the response (remove any markdown formatting)
+        if flashcard_text.startswith("```json"):
+            flashcard_text = flashcard_text[7:]
+        if flashcard_text.endswith("```"):
+            flashcard_text = flashcard_text[:-3]
+        
+        # Parse JSON
+        import json
+        try:
+            flashcard_data = json.loads(flashcard_text)
+            
+            # Validate and format the flashcards
+            flashcards = []
+            for item in flashcard_data:
+                if isinstance(item, dict) and "question" in item and "answer" in item:
+                    flashcards.append(Flashcard(
+                        question=item["question"].strip(),
+                        answer=item["answer"].strip()
+                    ))
+            
+            if len(flashcards) < 3:
+                raise ValueError("Not enough valid flashcards generated")
+            
+            return FlashcardResponse(
+                flashcards=flashcards,
+                success=True
+            )
+            
+        except (json.JSONDecodeError, ValueError) as e:
+            # Fallback: create simple flashcards from document chunks
+            flashcards = []
+            for i, chunk in enumerate(pdf_chunks[:8]):
+                if len(chunk.strip()) > 50:  # Only use substantial chunks
+                    flashcards.append(Flashcard(
+                        question=f"What is mentioned about: {chunk[:100]}...?",
+                        answer=chunk[:300] + "..." if len(chunk) > 300 else chunk
+                    ))
+            
+            return FlashcardResponse(
+                flashcards=flashcards,
+                success=True
+            )
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating flashcards: {str(e)}")
+
 # Entry point for running the application directly
 if __name__ == "__main__":
     import uvicorn
     # Start the server on all network interfaces (0.0.0.0) on port 8000
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000)
\ No newline at end of file
diff --git a/api/requirements.txt b/api/requirements.txt
index f2d9a1cbc..446a00c80 100644
--- a/api/requirements.txt
+++ b/api/requirements.txt
@@ -2,4 +2,6 @@ fastapi==0.115.12
 uvicorn==0.34.2
 openai==1.77.0
 pydantic==2.11.4
-python-multipart==0.0.18
\ No newline at end of file
+python-multipart==0.0.18
+PyPDF2==3.0.1
+numpy==1.26.4
\ No newline at end of file
diff --git a/api/vercel.json b/api/vercel.json
deleted file mode 100644
index b5f952634..000000000
--- a/api/vercel.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-    "version": 2,
-    "builds": [
-      { "src": "app.py", "use": "@vercel/python" }
-    ],
-    "routes": [
-      { "src": "/(.*)", "dest": "app.py" }
-    ]
-  } 
\ No newline at end of file
diff --git a/frontend/README.md b/frontend/README.md
index 56347bab6..fa5d2a833 100644
--- a/frontend/README.md
+++ b/frontend/README.md
@@ -1,3 +1,91 @@
-### Front End
+# AI Engineer Challenge Frontend
 
-Please populate this README with instructions on how to run the application!
\ No newline at end of file
+A beautiful, modern chat interface for your LLM application built with Next.js, TypeScript, and Tailwind CSS.
+
+## 🚀 Quick Start
+
+### Prerequisites
+
+- Node.js (version 18 or higher)
+- npm or yarn
+- The FastAPI backend running on `http://localhost:8000`
+
+### Installation
+
+1. Navigate to the frontend directory:
+   ```bash
+   cd frontend
+   ```
+
+2. Install dependencies:
+   ```bash
+   npm install
+   ```
+
+3. Start the development server:
+   ```bash
+   npm run dev
+   ```
+
+4. Open your browser and navigate to `http://localhost:3000`
+
+## 🎨 Features
+
+- **Modern UI**: Beautiful glass-morphism design with smooth animations
+- **Real-time Streaming**: Watch AI responses stream in real-time
+- **Settings Panel**: Configure your OpenAI API key, system prompt, and model
+- **Responsive Design**: Works perfectly on desktop and mobile devices
+- **TypeScript**: Full type safety for better development experience
+- **Tailwind CSS**: Utility-first CSS framework for rapid styling
+
+## 🔧 Configuration
+
+Before using the chat, you need to:
+
+1. Click the settings icon (⚙️) in the top-right corner
+2. Enter your OpenAI API key
+3. Optionally customize the system prompt and model
+4. Start chatting!
+
+## 🏗️ Project Structure
+
+```
+frontend/
+├── app/
+│   ├── globals.css          # Global styles and Tailwind imports
+│   ├── layout.tsx           # Root layout component
+│   └── page.tsx             # Main chat interface
+├── package.json             # Dependencies and scripts
+├── tailwind.config.js       # Tailwind CSS configuration
+├── tsconfig.json            # TypeScript configuration
+└── README.md               # This file
+```
+
+## 🚀 Deployment
+
+This frontend is ready to be deployed to Vercel:
+
+1. Push your code to GitHub
+2. Connect your repository to Vercel
+3. Deploy with one click!
+
+## 🔗 Backend Integration
+
+This frontend integrates with the FastAPI backend located in the `/api` directory. Make sure the backend is running on `http://localhost:8000` before using the frontend.
+
+The frontend communicates with the following backend endpoints:
+- `POST /api/chat` - Send messages and receive streaming responses
+- `GET /api/health` - Health check endpoint
+
+## 🎯 Next Steps
+
+1. Start the backend server (see `/api/README.md`)
+2. Install frontend dependencies and start the dev server
+3. Configure your OpenAI API key
+4. Start building amazing AI applications!
+
+## 🐛 Troubleshooting
+
+- **CORS Errors**: Make sure the backend CORS settings allow requests from `http://localhost:3000`
+- **API Key Issues**: Verify your OpenAI API key is valid and has sufficient credits
+- **Build Errors**: Ensure all dependencies are installed with `npm install`
\ No newline at end of file
diff --git a/frontend/app/globals.css b/frontend/app/globals.css
new file mode 100644
index 000000000..7d30b2028
--- /dev/null
+++ b/frontend/app/globals.css
@@ -0,0 +1,129 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+@layer base {
+  html {
+    font-family: 'Inter', system-ui, sans-serif;
+  }
+  
+  body {
+    @apply bg-black-950 text-white min-h-screen;
+  }
+}
+
+@layer components {
+  .glass-effect {
+    @apply bg-black-900/80 backdrop-blur-sm border border-black-800/20;
+  }
+  
+  /* 3D Flip Card Styles */
+  .perspective-1000 {
+    perspective: 1000px;
+  }
+  
+  .transform-style-preserve-3d {
+    transform-style: preserve-3d;
+  }
+  
+  .backface-hidden {
+    backface-visibility: hidden;
+  }
+  
+  .rotate-y-180 {
+    transform: rotateY(180deg);
+  }
+  
+  /* Flashcard hover effects */
+  .flashcard-container:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 10px 25px rgba(0, 0, 0, 0.3);
+  }
+  
+  /* Mobile touch optimization */
+  @media (max-width: 768px) {
+    .perspective-1000 {
+      perspective: 800px;
+    }
+  }
+  
+  /* Text truncation utility */
+  .line-clamp-6 {
+    display: -webkit-box;
+    -webkit-line-clamp: 6;
+    -webkit-box-orient: vertical;
+    overflow: hidden;
+  }
+  
+  .line-clamp-4 {
+    display: -webkit-box;
+    -webkit-line-clamp: 4;
+    -webkit-box-orient: vertical;
+    overflow: hidden;
+  }
+  
+  /* Modal animations */
+  .modal-enter {
+    opacity: 0;
+    transform: scale(0.9);
+  }
+  
+  .modal-enter-active {
+    opacity: 1;
+    transform: scale(1);
+    transition: opacity 200ms ease-out, transform 200ms ease-out;
+  }
+  
+  .gradient-text {
+    @apply bg-gradient-to-r from-cookie-500 to-cookie-700 bg-clip-text text-transparent;
+  }
+  
+  .chat-bubble {
+    @apply max-w-3xl mx-auto p-4 rounded-2xl shadow-lg;
+  }
+  
+  .user-bubble {
+    @apply bg-cookie-600 text-black-950 ml-auto;
+  }
+  
+  .ai-bubble {
+    @apply bg-black-900 text-white mr-auto border border-black-800;
+  }
+  
+  .typing-indicator {
+    @apply flex space-x-1 p-4;
+  }
+  
+  .typing-dot {
+    @apply w-2 h-2 bg-cookie-400 rounded-full animate-pulse;
+  }
+  
+  /* Study card flip animation */
+  .study-card {
+    transform-style: preserve-3d;
+    transition: transform 0.6s;
+  }
+  
+  .study-card.flipped {
+    transform: rotateY(180deg);
+  }
+  
+  .backface-hidden {
+    backface-visibility: hidden;
+  }
+  
+  .rotate-y-180 {
+    transform: rotateY(180deg);
+  }
+}
+
+@layer utilities {
+  .scrollbar-hide {
+    -ms-overflow-style: none;
+    scrollbar-width: none;
+  }
+  
+  .scrollbar-hide::-webkit-scrollbar {
+    display: none;
+  }
+} 
\ No newline at end of file
diff --git a/frontend/app/layout.tsx b/frontend/app/layout.tsx
new file mode 100644
index 000000000..645451970
--- /dev/null
+++ b/frontend/app/layout.tsx
@@ -0,0 +1,27 @@
+import React from 'react'
+import type { Metadata } from 'next'
+import { Inter } from 'next/font/google'
+import './globals.css'
+
+const inter = Inter({ subsets: ['latin'] })
+
+export const metadata: Metadata = {
+  title: 'CookiesPDF - Smart PDF Learning Assistant',
+  description: 'AI-powered PDF chat and flashcard generation for smart learning',
+}
+
+export default function RootLayout({
+  children,
+}: {
+  children: React.ReactNode
+}) {
+  return (
+    <html lang="en">
+      <body className={inter.className}>
+        <div className="min-h-screen bg-black-950">
+          {children}
+        </div>
+      </body>
+    </html>
+  )
+} 
\ No newline at end of file
diff --git a/frontend/app/page.tsx b/frontend/app/page.tsx
new file mode 100644
index 000000000..774fd9f64
--- /dev/null
+++ b/frontend/app/page.tsx
@@ -0,0 +1,1066 @@
+'use client'
+
+import React, { useState, useRef, useEffect } from 'react'
+import { Send, Bot, User, Settings, Sparkles, Loader2, Upload, FileText, X, Plus, CheckCircle, XCircle, HelpCircle } from 'lucide-react'
+
+interface Message {
+  id: string
+  content: string
+  role: 'user' | 'assistant'
+  timestamp: Date
+  citations?: Array<{
+    docName: string
+    page: number
+    snippet: string
+  }>
+}
+
+interface Flashcard {
+  question: string
+  answer: string
+}
+
+// Flashcard Component
+const FlashcardComponent: React.FC<{ 
+  card: Flashcard, 
+  onViewFull: (card: Flashcard) => void 
+}> = ({ card, onViewFull }) => {
+  const [isFlipped, setIsFlipped] = useState(false)
+
+  const handleCardClick = (e: React.MouseEvent) => {
+    e.stopPropagation()
+    setIsFlipped(!isFlipped)
+  }
+
+  const handleViewFullClick = (e: React.MouseEvent) => {
+    e.stopPropagation()
+    onViewFull(card)
+  }
+
+  return (
+    <div 
+      className="relative w-80 h-48 sm:w-full perspective-1000 flashcard-container transition-all duration-300"
+    >
+      <div 
+        className={`relative w-full h-full transition-transform duration-700 transform-style-preserve-3d ${
+          isFlipped ? 'rotate-y-180' : ''
+        }`}
+        style={{
+          transformStyle: 'preserve-3d',
+          transform: isFlipped ? 'rotateY(180deg)' : 'rotateY(0deg)',
+          transition: 'transform 0.7s ease-in-out'
+        }}
+      >
+        {/* Front of card (Question) */}
+        <div 
+          className="absolute w-full h-full bg-black-900 border border-black-700 rounded-lg shadow-md font-mono backface-hidden cursor-pointer"
+          style={{ backfaceVisibility: 'hidden' }}
+          onClick={handleCardClick}
+        >
+          <div className="flex flex-col items-center justify-center text-center h-full p-4">
+            <div className="text-green-400 font-bold mb-3 text-sm">Q:</div>
+            <div className="text-white text-sm leading-relaxed overflow-auto flex-1 flex items-center justify-center max-h-24">
+              <span className="line-clamp-6">{card.question}</span>
+            </div>
+            <div className="text-cookie-500 text-xs mt-2">
+              Click to reveal answer
+            </div>
+          </div>
+        </div>
+
+        {/* Back of card (Answer Preview) */}
+        <div 
+          className="absolute w-full h-full bg-black-800 border border-black-700 rounded-lg shadow-md font-mono backface-hidden rotate-y-180"
+          style={{ 
+            backfaceVisibility: 'hidden',
+            transform: 'rotateY(180deg)'
+          }}
+        >
+          <div className="flex flex-col items-center justify-center text-center h-full p-4">
+            <div className="text-blue-400 font-bold mb-3 text-sm">A:</div>
+            <div className="text-white text-sm leading-relaxed overflow-auto flex-1 flex items-center justify-center max-h-16">
+              <span className="line-clamp-4">{card.answer}</span>
+            </div>
+            <button
+              onClick={handleViewFullClick}
+              className="mt-2 px-3 py-1 bg-cookie-600 hover:bg-cookie-700 text-black text-xs rounded transition-colors"
+            >
+              View Full Answer
+            </button>
+            <div className="text-cookie-500 text-xs mt-1">
+              Click to see question
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  )
+}
+
+// Get API URL from environment or default to localhost
+const getApiUrl = () => {
+  if (typeof window !== 'undefined') {
+    // Client-side: use environment variable or default to FastAPI server
+    if (process.env.NODE_ENV === 'production') {
+      // In production, use the same domain for API calls (Python serverless function)
+      return `${window.location.origin}/api`
+    }
+    return process.env.NEXT_PUBLIC_API_URL || 'http://localhost:8000/api'
+  }
+  // Server-side: use environment variable or default
+  return process.env.NEXT_PUBLIC_API_URL || 'http://localhost:8000/api'
+}
+
+export default function Home() {
+  const [messages, setMessages] = useState<Message[]>([])
+  const [inputMessage, setInputMessage] = useState('')
+  const [isLoading, setIsLoading] = useState(false)
+  const [apiKey, setApiKey] = useState('')
+  const [developerMessage, setDeveloperMessage] = useState('You are a helpful AI assistant.')
+  const [model, setModel] = useState('gpt-4.1-mini')
+  const [showSettings, setShowSettings] = useState(false)
+  const [uploadedFile, setUploadedFile] = useState<File | null>(null)
+  const [isUploading, setIsUploading] = useState(false)
+  const [uploadStatus, setUploadStatus] = useState<string>('')
+  const [flashcards, setFlashcards] = useState<Flashcard[]>([])
+  const [isGeneratingFlashcards, setIsGeneratingFlashcards] = useState(false)
+  const [showFlashcards, setShowFlashcards] = useState(false)
+  const [selectedFlashcard, setSelectedFlashcard] = useState<Flashcard | null>(null)
+  const [showModal, setShowModal] = useState(false)
+  const [currentCardIndex, setCurrentCardIndex] = useState(0)
+  const [studyMode, setStudyMode] = useState(true)
+  const [selectedText, setSelectedText] = useState('')
+  const [showAddFlashcard, setShowAddFlashcard] = useState(false)
+  const [addFlashcardPosition, setAddFlashcardPosition] = useState({ x: 0, y: 0 })
+  const [studyProgress, setStudyProgress] = useState(0)
+  const [activeTopic, setActiveTopic] = useState<string | null>(null)
+  const [isStudyPanelOpen, setIsStudyPanelOpen] = useState(false)
+  const [sessionCards, setSessionCards] = useState<Flashcard[]>([])
+  const [gradedCount, setGradedCount] = useState(0)
+  const [showDone, setShowDone] = useState(false)
+  const messagesEndRef = useRef<HTMLDivElement>(null)
+  const fileInputRef = useRef<HTMLInputElement>(null)
+  const textSelectionRef = useRef<HTMLDivElement>(null)
+
+  const scrollToBottom = () => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' })
+  }
+
+  useEffect(() => {
+    scrollToBottom()
+  }, [messages])
+
+  useEffect(() => {
+    const handleClickOutside = () => {
+      setShowAddFlashcard(false)
+    }
+    
+    if (showAddFlashcard) {
+      document.addEventListener('click', handleClickOutside)
+      return () => document.removeEventListener('click', handleClickOutside)
+    }
+  }, [showAddFlashcard])
+
+  useEffect(() => {
+    const handleKeyDown = (e: KeyboardEvent) => {
+      if (e.key === 'Escape' && isStudyPanelOpen) {
+        setIsStudyPanelOpen(false)
+      }
+    }
+    
+    document.addEventListener('keydown', handleKeyDown)
+    return () => document.removeEventListener('keydown', handleKeyDown)
+  }, [isStudyPanelOpen])
+
+  // Handle topic changes - reset session
+  useEffect(() => {
+    if (isStudyPanelOpen && flashcards.length > 0) {
+      createSessionSnapshot()
+      if (activeTopic) {
+        // Simple toast notification
+        const toast = document.createElement('div')
+        toast.className = 'fixed top-4 right-4 bg-blue-600 text-white px-4 py-2 rounded-lg shadow-lg z-50'
+        toast.textContent = `Topic changed—session reset`
+        document.body.appendChild(toast)
+        setTimeout(() => {
+          document.body.removeChild(toast)
+        }, 3000)
+      }
+    }
+  }, [activeTopic, isStudyPanelOpen, flashcards])
+
+  // Handle panel opening - create new session
+  useEffect(() => {
+    if (isStudyPanelOpen && flashcards.length > 0) {
+      createSessionSnapshot()
+    }
+  }, [isStudyPanelOpen])
+
+  const handleFileUpload = async (file: File) => {
+    if (!file.name.endsWith('.pdf')) {
+      setUploadStatus('Please select a PDF file')
+      return
+    }
+
+    if (!apiKey.trim()) {
+      setUploadStatus('Please set your API key first')
+      return
+    }
+
+    setIsUploading(true)
+    setUploadStatus('Uploading and processing PDF...')
+
+    try {
+      const formData = new FormData()
+      formData.append('file', file)
+
+      const apiUrl = getApiUrl()
+      const response = await fetch(`${apiUrl}/upload-pdf`, {
+        method: 'POST',
+        headers: {
+          'Authorization': `Bearer ${apiKey}`,
+        },
+        body: formData,
+      })
+
+      if (!response.ok) {
+        const errorData = await response.json()
+        throw new Error(errorData.detail || 'Upload failed')
+      }
+
+      const result = await response.json()
+      setUploadedFile(file)
+      setUploadStatus(result.message)
+      // Clear previous flashcards when new PDF is uploaded
+      setFlashcards([])
+      setShowFlashcards(false)
+    } catch (error) {
+      console.error('Upload error:', error)
+      setUploadStatus(`Upload failed: ${error instanceof Error ? error.message : 'Unknown error'}`)
+    } finally {
+      setIsUploading(false)
+    }
+  }
+
+  const handleFileSelect = (e: React.ChangeEvent<HTMLInputElement>) => {
+    const file = e.target.files?.[0]
+    if (file) {
+      handleFileUpload(file)
+    }
+  }
+
+  const removeUploadedFile = () => {
+    setUploadedFile(null)
+    setUploadStatus('')
+    setFlashcards([])
+    setShowFlashcards(false)
+    if (fileInputRef.current) {
+      fileInputRef.current.value = ''
+    }
+  }
+
+  const generateFlashcards = async () => {
+    if (!apiKey.trim()) {
+      alert('Please set your API key first')
+      return
+    }
+
+    setIsGeneratingFlashcards(true)
+    try {
+      const apiUrl = getApiUrl()
+      const response = await fetch(`${apiUrl}/flashcards`, {
+        method: 'POST',
+        headers: {
+          'Authorization': `Bearer ${apiKey}`,
+          'Content-Type': 'application/json',
+        },
+      })
+
+      if (!response.ok) {
+        const errorData = await response.json()
+        throw new Error(errorData.detail || 'Failed to generate flashcards')
+      }
+
+      const result = await response.json()
+      setFlashcards(result.flashcards)
+      setShowFlashcards(true)
+      setIsStudyPanelOpen(true)
+      // Session will be created by useEffect when panel opens
+    } catch (error) {
+      console.error('Flashcard generation error:', error)
+      alert(`Error generating flashcards: ${error instanceof Error ? error.message : 'Unknown error'}`)
+    } finally {
+      setIsGeneratingFlashcards(false)
+    }
+  }
+
+  const handleViewFull = (card: Flashcard) => {
+    setSelectedFlashcard(card)
+    setShowModal(true)
+  }
+
+  const closeModal = () => {
+    setShowModal(false)
+    setSelectedFlashcard(null)
+  }
+
+  const handleTextSelection = (e: React.MouseEvent) => {
+    e.stopPropagation()
+    const selection = window.getSelection()
+    if (selection && selection.toString().trim()) {
+      const rect = selection.getRangeAt(0).getBoundingClientRect()
+      setSelectedText(selection.toString())
+      setAddFlashcardPosition({
+        x: rect.left + rect.width / 2,
+        y: rect.top - 10
+      })
+      setShowAddFlashcard(true)
+    } else {
+      setShowAddFlashcard(false)
+    }
+  }
+
+  const handleAddFlashcard = (text: string) => {
+    // Create a simple flashcard from selected text
+    const newFlashcard: Flashcard = {
+      question: `What does this mean: "${text.substring(0, 100)}${text.length > 100 ? '...' : ''}"?`,
+      answer: text
+    }
+    setFlashcards(prev => [...prev, newFlashcard])
+    setShowAddFlashcard(false)
+    setSelectedText('')
+  }
+
+  const handleCardGrading = (grade: 1 | 2 | 3) => {
+    if (showDone) return // Don't allow grading when done view is showing
+    
+    const currentSession = getCurrentSessionCards()
+    const newGradedCount = gradedCount + 1
+    
+    setGradedCount(newGradedCount)
+    
+    if (newGradedCount >= currentSession.length) {
+      // Session completed - show done view
+      setShowDone(true)
+    } else {
+      // Move to next card
+      setCurrentCardIndex(prev => Math.min(prev + 1, currentSession.length - 1))
+    }
+  }
+
+  const handleKeyPress = (e: React.KeyboardEvent) => {
+    if (showDone) return // Don't allow grading when done view is showing
+    if (e.key === '1') handleCardGrading(1)
+    if (e.key === '2') handleCardGrading(2)
+    if (e.key === '3') handleCardGrading(3)
+  }
+
+  // Helper function to check if a card matches the active topic
+  const matchesTopic = (card: Flashcard, topic: string) => {
+    const bag = [
+      card.question.toLowerCase(),
+      card.answer.toLowerCase()
+    ]
+    return bag.some(text => text.includes(topic.toLowerCase()))
+  }
+
+  // Get filtered flashcards based on active topic
+  const getFilteredFlashcards = () => {
+    if (!activeTopic) return flashcards
+    return flashcards.filter(card => matchesTopic(card, activeTopic))
+  }
+
+  // Create a new session snapshot
+  const createSessionSnapshot = () => {
+    const filtered = getFilteredFlashcards()
+    const snapshot = filtered.slice(0, 9)
+    setSessionCards(snapshot)
+    setCurrentCardIndex(0)
+    setGradedCount(0)
+    setShowDone(false)
+    return snapshot
+  }
+
+  // Ensure current card index is within bounds
+  const getCurrentCardIndex = () => {
+    const session = getCurrentSessionCards()
+    return Math.max(0, Math.min(currentCardIndex, session.length - 1))
+  }
+
+  // Get current session cards (frozen snapshot)
+  const getCurrentSessionCards = () => {
+    return sessionCards
+  }
+
+  // Handle slash commands
+  const handleSlashCommand = (input: string) => {
+    if (input.startsWith('/topic ')) {
+      const topic = input.replace('/topic ', '').trim()
+      setActiveTopic(topic)
+      return true
+    }
+    if (input === '/clear-topic') {
+      setActiveTopic(null)
+      return true
+    }
+    return false
+  }
+
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault()
+    if (!inputMessage.trim() || !apiKey.trim()) return
+
+    // Handle slash commands
+    if (handleSlashCommand(inputMessage)) {
+      setInputMessage('')
+      return
+    }
+
+    const userMessage: Message = {
+      id: Date.now().toString(),
+      content: inputMessage,
+      role: 'user',
+      timestamp: new Date()
+    }
+
+    setMessages(prev => [...prev, userMessage])
+    setInputMessage('')
+    setIsLoading(true)
+
+    try {
+      const apiUrl = getApiUrl()
+      const response = await fetch(`${apiUrl}/chat`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'Authorization': `Bearer ${apiKey}`,
+        },
+        body: JSON.stringify({
+          developer_message: developerMessage,
+          user_message: inputMessage,
+          model: model,
+        }),
+      })
+
+      if (!response.ok) {
+        throw new Error('Failed to get response')
+      }
+
+      const reader = response.body?.getReader()
+      if (!reader) throw new Error('No reader available')
+
+      let aiResponse = ''
+      let controlParsed = false
+      const aiMessage: Message = {
+        id: (Date.now() + 1).toString(),
+        content: '',
+        role: 'assistant',
+        timestamp: new Date()
+      }
+
+      setMessages(prev => [...prev, aiMessage])
+
+      while (true) {
+        const { done, value } = await reader.read()
+        if (done) break
+
+        const chunk = new TextDecoder().decode(value)
+        aiResponse += chunk
+
+        // Parse CONTROL line if not already parsed
+        if (!controlParsed && aiResponse.includes('\n')) {
+          const lines = aiResponse.split('\n')
+          if (lines[0].startsWith('CONTROL:')) {
+            try {
+              const controlText = lines[0].replace('CONTROL:', '').trim()
+              const control = JSON.parse(controlText)
+              if (control?.action === 'set_topic' && control?.topic) {
+                setActiveTopic(control.topic)
+                // Remove control line from visible content
+                aiResponse = lines.slice(1).join('\n')
+              }
+            } catch (e) {
+              // Ignore parsing errors
+            }
+            controlParsed = true
+          }
+        }
+
+        setMessages(prev => 
+          prev.map(msg => 
+            msg.id === aiMessage.id 
+              ? { ...msg, content: aiResponse }
+              : msg
+          )
+        )
+      }
+    } catch (error) {
+      console.error('Error:', error)
+      const errorMessage: Message = {
+        id: (Date.now() + 1).toString(),
+        content: 'Sorry, there was an error processing your request. Please check your API key and try again.',
+        role: 'assistant',
+        timestamp: new Date()
+      }
+      setMessages(prev => [...prev, errorMessage])
+    } finally {
+      setIsLoading(false)
+    }
+  }
+
+  return (
+    <div className="min-h-screen flex flex-col">
+      {/* Header */}
+      <header className="sticky top-0 z-40 bg-black-950/90 backdrop-blur border-b border-black-800">
+        <div className="max-w-[1200px] mx-auto px-4 py-2 flex items-center justify-between">
+          <div className="flex items-center space-x-3">
+            <div className="w-8 h-8 bg-gradient-to-r from-cookie-500 to-cookie-600 rounded-full flex items-center justify-center relative">
+              <div className="w-5 h-5 bg-cookie-200 rounded-full relative">
+                <div className="absolute top-0.5 left-0.5 w-0.5 h-0.5 bg-chocolate-800 rounded-full"></div>
+                <div className="absolute top-1 right-0.5 w-0.5 h-0.5 bg-chocolate-800 rounded-full"></div>
+                <div className="absolute bottom-0.5 left-1 w-0.5 h-0.5 bg-chocolate-800 rounded-full"></div>
+                <div className="absolute bottom-1 right-1 w-0.5 h-0.5 bg-chocolate-800 rounded-full"></div>
+              </div>
+            </div>
+            <h1 className="text-lg font-bold text-white">CookiesPDF</h1>
+          </div>
+          <div className="flex items-center space-x-3">
+            <button
+              onClick={generateFlashcards}
+              disabled={isGeneratingFlashcards || !apiKey.trim() || !uploadedFile}
+              className="rounded-xl px-4 py-2 font-medium shadow-sm bg-cookie-600 text-black hover:bg-cookie-500 disabled:opacity-50 disabled:cursor-not-allowed transition-colors flex items-center space-x-2"
+            >
+              {isGeneratingFlashcards ? (
+                <Loader2 className="w-4 h-4 animate-spin" />
+              ) : (
+                <Sparkles className="w-4 h-4" />
+              )}
+              <span>Generate Flashcards</span>
+            </button>
+            <button
+              onClick={() => setShowSettings(!showSettings)}
+              className="p-2 rounded-lg hover:bg-black-800 transition-colors"
+            >
+              <Settings className="w-5 h-5 text-cookie-300" />
+            </button>
+          </div>
+        </div>
+      </header>
+
+      {/* Settings Modal */}
+      {showSettings && (
+        <div className="fixed inset-0 bg-black-950 bg-opacity-60 flex items-center justify-center z-50 p-4">
+          <div className="bg-black-900 rounded-2xl border border-black-700 shadow-xl max-w-2xl w-full max-h-[80vh] overflow-hidden">
+            <div className="flex items-center justify-between p-4 border-b border-black-700">
+              <h3 className="text-lg font-semibold text-white">Settings</h3>
+              <button
+                onClick={() => setShowSettings(false)}
+                className="text-cookie-400 hover:text-cookie-200 transition-colors p-2 rounded-lg hover:bg-black-800"
+              >
+                <X className="w-5 h-5" />
+              </button>
+            </div>
+            <div className="p-6 space-y-4 overflow-y-auto max-h-[calc(80vh-80px)]">
+              <div>
+                <label className="block text-sm font-medium text-cookie-300 mb-2">
+                  OpenAI API Key
+                </label>
+                <input
+                  type="password"
+                  value={apiKey}
+                  onChange={(e) => setApiKey(e.target.value)}
+                  placeholder="sk-..."
+                  className="w-full px-3 py-2 bg-black-800 border border-black-700 rounded-lg focus:ring-2 focus:ring-cookie-500 focus:border-transparent text-white placeholder-cookie-400"
+                />
+              </div>
+              <div>
+                <label className="block text-sm font-medium text-cookie-300 mb-2">
+                  Developer Message (System Prompt)
+                </label>
+                <textarea
+                  value={developerMessage}
+                  onChange={(e) => setDeveloperMessage(e.target.value)}
+                  placeholder="You are a helpful AI assistant..."
+                  rows={3}
+                  className="w-full px-3 py-2 bg-black-800 border border-black-700 rounded-lg focus:ring-2 focus:ring-cookie-500 focus:border-transparent text-white placeholder-cookie-400"
+                />
+              </div>
+              <div>
+                <label className="block text-sm font-medium text-cookie-300 mb-2">
+                  Model
+                </label>
+                <select
+                  value={model}
+                  onChange={(e) => setModel(e.target.value)}
+                  className="w-full px-3 py-2 bg-black-800 border border-black-700 rounded-lg focus:ring-2 focus:ring-cookie-500 focus:border-transparent text-white"
+                >
+                  <option value="gpt-4.1-mini">GPT-4.1 Mini</option>
+                  <option value="gpt-4">GPT-4</option>
+                  <option value="gpt-3.5-turbo">GPT-3.5 Turbo</option>
+                </select>
+              </div>
+              
+              {/* PDF Upload Section */}
+              <div className="border-t border-black-700 pt-4">
+                <label className="block text-sm font-medium text-cookie-300 mb-2">
+                  Upload PDF for RAG Chat
+                </label>
+                
+                {!uploadedFile ? (
+                  <div className="space-y-3">
+                    <div
+                      className="border-2 border-dashed border-black-700 rounded-lg p-6 text-center hover:border-cookie-400 transition-colors cursor-pointer"
+                      onClick={() => fileInputRef.current?.click()}
+                    >
+                      <Upload className="w-8 h-8 text-cookie-400 mx-auto mb-2" />
+                      <p className="text-sm text-cookie-300">
+                        Click to upload a PDF file
+                      </p>
+                      <p className="text-xs text-cookie-400 mt-1">
+                        The AI will only answer questions using information from this document
+                      </p>
+                    </div>
+                    <input
+                      ref={fileInputRef}
+                      type="file"
+                      accept=".pdf"
+                      onChange={handleFileSelect}
+                      className="hidden"
+                    />
+                  </div>
+                ) : (
+                  <div className="bg-cookie-900/20 border border-cookie-600/30 rounded-lg p-4">
+                    <div className="flex items-center justify-between">
+                      <div className="flex items-center space-x-3">
+                        <FileText className="w-5 h-5 text-cookie-400" />
+                        <div>
+                          <p className="text-sm font-medium text-cookie-200">
+                            {uploadedFile.name}
+                          </p>
+                          <p className="text-xs text-cookie-300">
+                            Ready for RAG chat
+                          </p>
+                        </div>
+                      </div>
+                      <button
+                        onClick={removeUploadedFile}
+                        className="p-1 hover:bg-cookie-800/30 rounded transition-colors"
+                      >
+                        <X className="w-4 h-4 text-cookie-400" />
+                      </button>
+                    </div>
+                  </div>
+                )}
+                
+                {uploadStatus && (
+                  <div className={`mt-2 text-sm ${
+                    uploadStatus.includes('successfully') 
+                      ? 'text-green-400' 
+                      : uploadStatus.includes('failed') || uploadStatus.includes('Please')
+                      ? 'text-red-400'
+                      : 'text-cookie-400'
+                  }`}>
+                    {uploadStatus}
+                  </div>
+                )}
+                
+                {isUploading && (
+                  <div className="flex items-center space-x-2 mt-2">
+                    <Loader2 className="w-4 h-4 animate-spin text-cookie-400" />
+                    <span className="text-sm text-cookie-400">Processing PDF...</span>
+                  </div>
+                )}
+              </div>
+            </div>
+          </div>
+        </div>
+      )}
+
+      {/* Chat Messages */}
+      <div className="flex-1 overflow-y-auto p-4 bg-black-950">
+        <div className="max-w-4xl mx-auto space-y-4">
+          {messages.length === 0 && (
+            <div className="text-center py-12">
+              <div className="w-16 h-16 bg-gradient-to-r from-cookie-500 to-cookie-600 rounded-full flex items-center justify-center mx-auto mb-4">
+                <Bot className="w-8 h-8 text-black" />
+              </div>
+              <h3 className="text-xl font-semibold text-white mb-2">
+                Welcome to CookiesPDF!
+              </h3>
+              <p className="text-cookie-300 max-w-md mx-auto">
+                Upload a PDF to start learning with AI-powered chat and flashcards. Make sure to set up your API key in the settings above.
+              </p>
+            </div>
+          )}
+
+          {messages.map((message) => (
+            <div
+              key={message.id}
+              className={`chat-bubble ${
+                message.role === 'user' ? 'user-bubble' : 'ai-bubble'
+              } animate-fade-in`}
+            >
+              <div className="flex items-start space-x-3">
+                <div className={`w-8 h-8 rounded-full flex items-center justify-center flex-shrink-0 ${
+                  message.role === 'user' 
+                    ? 'bg-cookie-600' 
+                    : 'bg-black-800'
+                }`}>
+                  {message.role === 'user' ? (
+                    <User className="w-4 h-4 text-black" />
+                  ) : (
+                    <Bot className="w-4 h-4 text-cookie-400" />
+                  )}
+                </div>
+                <div className="flex-1">
+                  <p className="text-sm font-medium mb-1 text-cookie-300">
+                    {message.role === 'user' ? 'You' : 'AI Assistant'}
+                  </p>
+                  <div 
+                    className={`prose prose-sm max-w-none ${
+                      message.role === 'assistant' ? 'max-w-[680px] mx-auto prose-slate' : ''
+                    }`}
+                    onMouseUp={message.role === 'assistant' ? handleTextSelection : undefined}
+                    style={{ userSelect: message.role === 'assistant' ? 'text' : 'none' }}
+                  >
+                    {message.content.split('\n').map((line, index) => (
+                      <p key={index} className="mb-2 last:mb-0 text-white">
+                        {line}
+                      </p>
+                    ))}
+                  </div>
+                  
+                  {/* Citation chips for assistant messages */}
+                  {message.role === 'assistant' && message.citations && message.citations.length > 0 && (
+                    <div className="flex flex-wrap gap-2 mt-3">
+                      {message.citations.slice(0, 3).map((citation, index) => (
+                        <div key={index} className="relative group">
+                          <div className="inline-flex items-center rounded-full px-2.5 py-1 text-xs bg-cookie-900/30 hover:bg-cookie-800/40 transition-colors cursor-pointer border border-cookie-600/30">
+                            <span className="text-cookie-200">
+                              {citation.docName} (p. {citation.page})
+                            </span>
+                          </div>
+                          {/* Hover popover */}
+                          <div className="absolute bottom-full left-0 mb-2 w-64 p-3 bg-black-800 border border-black-700 rounded-lg shadow-lg opacity-0 group-hover:opacity-100 transition-opacity duration-200 z-10">
+                            <div className="text-xs text-cookie-300 line-clamp-4">
+                              {citation.snippet.length > 180 
+                                ? `${citation.snippet.substring(0, 180)}...` 
+                                : citation.snippet
+                              }
+                            </div>
+                          </div>
+                        </div>
+                      ))}
+                    </div>
+                  )}
+                  
+                  <p className="text-xs text-cookie-500 mt-2">
+                    {message.timestamp.toLocaleTimeString()}
+                  </p>
+                </div>
+              </div>
+            </div>
+          ))}
+
+          {isLoading && (
+            <div className="chat-bubble ai-bubble animate-fade-in">
+              <div className="flex items-start space-x-3">
+                <div className="w-8 h-8 bg-black-800 rounded-full flex items-center justify-center flex-shrink-0">
+                  <Bot className="w-4 h-4 text-cookie-400" />
+                </div>
+                <div className="flex-1">
+                  <p className="text-sm font-medium mb-2 text-cookie-300">AI Assistant</p>
+                  <div className="typing-indicator">
+                    <div className="typing-dot bg-cookie-400"></div>
+                    <div className="typing-dot bg-cookie-400" style={{ animationDelay: '0.2s' }}></div>
+                    <div className="typing-dot bg-cookie-400" style={{ animationDelay: '0.4s' }}></div>
+                  </div>
+                </div>
+              </div>
+            </div>
+          )}
+          
+          <div ref={messagesEndRef} />
+        </div>
+      </div>
+
+      {/* Study Mode Panel */}
+      {showFlashcards && flashcards.length > 0 && isStudyPanelOpen && (
+        <div className="fixed right-0 top-0 h-full w-96 bg-black-900 border-l border-black-700 shadow-xl z-30">
+          <div className="h-full flex flex-col">
+            {/* Header */}
+            <div className="flex items-center justify-between p-4 border-b border-black-700">
+              <h3 className="text-lg font-semibold text-white">Study Mode</h3>
+              <button
+                onClick={() => setIsStudyPanelOpen(false)}
+                className="text-cookie-400 hover:text-cookie-200 transition-colors p-2 rounded-lg hover:bg-black-800"
+              >
+                <X className="w-5 h-5" />
+              </button>
+            </div>
+            
+            {/* Topic Info */}
+            {activeTopic && (
+              <div className="px-4 py-2 border-b border-black-700 bg-cookie-900/20">
+                <div className="text-sm text-cookie-200">
+                  <span className="font-medium">Topic:</span> {activeTopic}
+                </div>
+                <div className="text-xs text-cookie-300">
+                  {getCurrentSessionCards().length} cards in session
+                </div>
+              </div>
+            )}
+
+            {/* Progress Bar */}
+            <div className="px-4 py-2 border-b border-black-700">
+              <div className="flex items-center justify-between text-sm text-cookie-300 mb-2">
+                <span>Progress</span>
+                <span>{gradedCount} / {getCurrentSessionCards().length}</span>
+              </div>
+              <div className="w-full bg-black-800 rounded-full h-2">
+                <div 
+                  className="bg-cookie-600 h-2 rounded-full transition-all duration-300"
+                  style={{ width: `${getCurrentSessionCards().length > 0 ? (gradedCount / getCurrentSessionCards().length) * 100 : 0}%` }}
+                ></div>
+              </div>
+            </div>
+            
+            {/* Study Card */}
+            <div className="flex-1 p-4 flex flex-col items-center justify-center">
+              {showDone ? (
+                <div className="text-center">
+                  <div className="text-2xl mb-4">🎉</div>
+                  <div className="text-lg font-semibold text-white mb-2">Session Complete!</div>
+                  <div className="text-sm text-cookie-300 mb-6">
+                    You've reviewed all {getCurrentSessionCards().length} cards
+                  </div>
+                  <div className="space-y-2">
+                    <button
+                      onClick={() => {
+                        setCurrentCardIndex(0)
+                        setGradedCount(0)
+                        setShowDone(false)
+                      }}
+                      className="w-full rounded-xl px-4 py-2 font-medium shadow-sm bg-cookie-600 text-black hover:bg-cookie-500 transition-colors"
+                    >
+                      Review Again
+                    </button>
+                    <button
+                      onClick={() => setIsStudyPanelOpen(false)}
+                      className="w-full rounded-xl px-4 py-2 font-medium shadow-sm bg-black-700 text-cookie-200 hover:bg-black-600 transition-colors"
+                    >
+                      Close
+                    </button>
+                  </div>
+                </div>
+              ) : getCurrentSessionCards().length > 0 ? (
+                <div className="w-full max-w-sm">
+                  <div 
+                    className="study-card relative w-full h-64 bg-black-800 border border-black-700 rounded-2xl shadow-sm cursor-pointer transition-transform duration-300 hover:shadow-md"
+                    onClick={() => {
+                      const card = document.querySelector('.study-card')
+                      if (card) {
+                        card.classList.toggle('flipped')
+                      }
+                    }}
+                  >
+                    <div className="absolute inset-0 p-6 flex flex-col items-center justify-center text-center backface-hidden">
+                      <div className="text-cookie-400 font-bold mb-3 text-sm">Q:</div>
+                      <div className="text-white text-sm leading-relaxed">
+                        {getCurrentSessionCards()[getCurrentCardIndex()]?.question}
+                      </div>
+                    </div>
+                    <div className="absolute inset-0 p-6 flex flex-col items-center justify-center text-center bg-black-700 rounded-2xl rotate-y-180 backface-hidden">
+                      <div className="text-green-400 font-bold mb-3 text-sm">A:</div>
+                      <div className="text-white text-sm leading-relaxed">
+                        {getCurrentSessionCards()[getCurrentCardIndex()]?.answer}
+                      </div>
+                    </div>
+                  </div>
+                  
+                  {/* Grading Buttons */}
+                  <div className="mt-6 space-y-3">
+                    <div className="text-sm text-cookie-300 text-center mb-3">
+                      How well did you know this?
+                    </div>
+                    <div className="flex space-x-2">
+                      <button
+                        onClick={() => handleCardGrading(1)}
+                        onKeyDown={handleKeyPress}
+                        className="flex-1 flex items-center justify-center space-x-2 px-3 py-2 bg-green-900/30 text-green-400 rounded-lg hover:bg-green-800/40 transition-colors border border-green-600/30"
+                      >
+                        <CheckCircle className="w-4 h-4" />
+                        <span className="text-sm font-medium">Knew (1)</span>
+                      </button>
+                      <button
+                        onClick={() => handleCardGrading(2)}
+                        onKeyDown={handleKeyPress}
+                        className="flex-1 flex items-center justify-center space-x-2 px-3 py-2 bg-yellow-900/30 text-yellow-400 rounded-lg hover:bg-yellow-800/40 transition-colors border border-yellow-600/30"
+                      >
+                        <HelpCircle className="w-4 h-4" />
+                        <span className="text-sm font-medium">Unsure (2)</span>
+                      </button>
+                      <button
+                        onClick={() => handleCardGrading(3)}
+                        onKeyDown={handleKeyPress}
+                        className="flex-1 flex items-center justify-center space-x-2 px-3 py-2 bg-red-900/30 text-red-400 rounded-lg hover:bg-red-800/40 transition-colors border border-red-600/30"
+                      >
+                        <XCircle className="w-4 h-4" />
+                        <span className="text-sm font-medium">Didn't know (3)</span>
+                      </button>
+                    </div>
+                    <div className="text-xs text-cookie-400 text-center mt-2">
+                      Use keyboard shortcuts: 1, 2, or 3
+                    </div>
+                  </div>
+                </div>
+              ) : (
+                <div className="text-center">
+                  <div className="text-cookie-400 mb-4">
+                    {activeTopic ? 'No cards for this topic' : 'No flashcards available'}
+                  </div>
+                  {activeTopic && (
+                    <button
+                      onClick={() => setActiveTopic(null)}
+                      className="rounded-xl px-4 py-2 font-medium shadow-sm bg-cookie-600 text-black hover:bg-cookie-500 transition-colors"
+                    >
+                      Clear Topic
+                    </button>
+                  )}
+                </div>
+              )}
+            </div>
+          </div>
+        </div>
+      )}
+
+      {/* Input Form */}
+      <div className="sticky bottom-0 bg-black-950/90 backdrop-blur border-t border-black-800 p-4">
+        <div className="max-w-4xl mx-auto">
+          {/* Topic Pill */}
+          {activeTopic && (
+            <div className="mb-3 flex items-center justify-center">
+              <div className="inline-flex items-center gap-1 rounded-full px-2.5 py-1 text-xs bg-cookie-900/30 border border-cookie-600/30">
+                <span className="text-cookie-200">Focused: {activeTopic}</span>
+                <button
+                  onClick={() => setActiveTopic(null)}
+                  className="ml-1 text-cookie-400 hover:text-cookie-200 transition-colors"
+                >
+                  ✕
+                </button>
+              </div>
+            </div>
+          )}
+          
+          <form onSubmit={handleSubmit} className="flex space-x-4">
+            <div className="flex-1">
+              <input
+                type="text"
+                value={inputMessage}
+                onChange={(e) => setInputMessage(e.target.value)}
+                onKeyDown={(e) => {
+                  if (e.key === 'Enter' && !e.shiftKey) {
+                    e.preventDefault()
+                    handleSubmit(e)
+                  }
+                }}
+                placeholder="Type your message here... (Enter to send, Shift+Enter for new line)"
+                disabled={isLoading || !apiKey.trim()}
+                className="w-full px-4 py-3 bg-black-800 border border-black-700 rounded-xl focus:ring-2 focus:ring-cookie-500 focus:border-transparent disabled:opacity-50 disabled:cursor-not-allowed text-white placeholder-cookie-400"
+              />
+            </div>
+            <button
+              type="submit"
+              disabled={isLoading || !inputMessage.trim() || !apiKey.trim()}
+              className="px-6 py-3 bg-cookie-600 text-black rounded-xl hover:bg-cookie-500 focus:ring-2 focus:ring-cookie-500 focus:ring-offset-2 disabled:opacity-50 disabled:cursor-not-allowed transition-all duration-200 flex items-center space-x-2"
+            >
+              {isLoading ? (
+                <Loader2 className="w-5 h-5 animate-spin" />
+              ) : (
+                <Send className="w-5 h-5" />
+              )}
+              <span>Send</span>
+            </button>
+          </form>
+          
+          {!apiKey.trim() && (
+            <p className="text-sm text-cookie-500 mt-2 text-center">
+              Please set your OpenAI API key in the settings to start chatting
+            </p>
+          )}
+        </div>
+      </div>
+
+      {/* Floating Add Flashcard Button */}
+      {showAddFlashcard && selectedText && (
+        <div
+          className="fixed z-50"
+          style={{
+            left: `${addFlashcardPosition.x}px`,
+            top: `${addFlashcardPosition.y}px`,
+            transform: 'translateX(-50%)'
+          }}
+          onClick={(e) => e.stopPropagation()}
+        >
+          <button
+            onClick={(e) => {
+              e.stopPropagation()
+              handleAddFlashcard(selectedText)
+            }}
+            className="flex items-center space-x-2 px-3 py-2 bg-cookie-600 text-black rounded-lg shadow-lg hover:bg-cookie-500 transition-colors"
+          >
+            <Plus className="w-4 h-4" />
+            <span className="text-sm font-medium">Add Flashcard</span>
+          </button>
+        </div>
+      )}
+
+      {/* Flashcard Modal */}
+      {showModal && selectedFlashcard && (
+        <div className="fixed inset-0 bg-black-950 bg-opacity-50 flex items-center justify-center z-50 p-4">
+          <div className="bg-black-900 border border-black-700 rounded-lg shadow-xl max-w-2xl w-full max-h-[80vh] overflow-hidden">
+            {/* Modal Header */}
+            <div className="flex items-center justify-between p-4 border-b border-black-700">
+              <h3 className="text-lg font-semibold text-white">Flashcard Details</h3>
+              <button
+                onClick={closeModal}
+                className="text-cookie-400 hover:text-cookie-200 transition-colors p-2 rounded-lg hover:bg-black-800"
+              >
+                <X className="w-5 h-5" />
+              </button>
+            </div>
+            
+            {/* Modal Content */}
+            <div className="p-6 overflow-y-auto max-h-[calc(80vh-80px)]">
+              {/* Question */}
+              <div className="mb-6">
+                <div className="text-green-400 font-bold mb-3 text-sm">Question:</div>
+                <div className="bg-black-800 border border-black-700 rounded-lg p-4 font-mono text-white text-sm leading-relaxed">
+                  {selectedFlashcard.question}
+                </div>
+              </div>
+              
+              {/* Answer */}
+              <div>
+                <div className="text-blue-400 font-bold mb-3 text-sm">Answer:</div>
+                <div className="bg-black-800 border border-black-700 rounded-lg p-4 font-mono text-white text-sm leading-relaxed whitespace-pre-wrap">
+                  {selectedFlashcard.answer}
+                </div>
+              </div>
+            </div>
+            
+            {/* Modal Footer */}
+            <div className="flex justify-end p-4 border-t border-black-700">
+              <button
+                onClick={closeModal}
+                className="px-4 py-2 bg-black-700 hover:bg-black-600 text-white rounded-lg transition-colors"
+              >
+                Close
+              </button>
+            </div>
+          </div>
+        </div>
+      )}
+    </div>
+  )
+} 
\ No newline at end of file
diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts
new file mode 100644
index 000000000..40c3d6809
--- /dev/null
+++ b/frontend/next-env.d.ts
@@ -0,0 +1,5 @@
+/// <reference types="next" />
+/// <reference types="next/image-types/global" />
+
+// NOTE: This file should not be edited
+// see https://nextjs.org/docs/app/building-your-application/configuring/typescript for more information.
diff --git a/frontend/next.config.js b/frontend/next.config.js
new file mode 100644
index 000000000..08d3b6fea
--- /dev/null
+++ b/frontend/next.config.js
@@ -0,0 +1,7 @@
+/** @type {import('next').NextConfig} */
+const nextConfig = {
+  reactStrictMode: true, // helps catch bugs in development
+  swcMinify: true,       // enables faster builds with SWC compiler
+}
+
+module.exports = nextConfig
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
new file mode 100644
index 000000000..fc2e81edd
--- /dev/null
+++ b/frontend/package-lock.json
@@ -0,0 +1,6030 @@
+{
+  "name": "ai-engineer-challenge-frontend",
+  "version": "0.1.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "ai-engineer-challenge-frontend",
+      "version": "0.1.0",
+      "dependencies": {
+        "autoprefixer": "^10.0.1",
+        "lucide-react": "^0.294.0",
+        "next": "^14.2.32",
+        "postcss": "^8",
+        "react": "^18",
+        "react-dom": "^18",
+        "tailwindcss": "^3.3.0"
+      },
+      "devDependencies": {
+        "@types/node": "^20",
+        "@types/react": "^18",
+        "@types/react-dom": "^18",
+        "eslint": "^8",
+        "eslint-config-next": "14.0.4",
+        "typescript": "^5"
+      }
+    },
+    "node_modules/@alloc/quick-lru": {
+      "version": "5.2.0",
+      "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
+      "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/@emnapi/core": {
+      "version": "1.5.0",
+      "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.5.0.tgz",
+      "integrity": "sha512-sbP8GzB1WDzacS8fgNPpHlp6C9VZe+SJP3F90W9rLemaQj2PzIuTEl1qDOYQf58YIpyjViI24y9aPWCjEzY2cg==",
+      "dev": true,
+      "license": "MIT",
+      "optional": true,
+      "dependencies": {
+        "@emnapi/wasi-threads": "1.1.0",
+        "tslib": "^2.4.0"
+      }
+    },
+    "node_modules/@emnapi/runtime": {
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+      "integrity": "sha512-gmBGslpoQJtgnMAvOVqGZpEz9dyoKTCzy2nfz/n8aIFhN/jCE/rCmcxabB6jOOHV+0WNnylOxaxBQPSvcWklhA==",
+      "license": "MIT",
+      "dependencies": {
+        "ansi-regex": "^6.0.1"
+      },
+      "engines": {
+        "node": ">=12"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/strip-ansi?sponsor=1"
+      }
+    },
+    "node_modules/wrappy": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
+      "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==",
+      "dev": true,
+      "license": "ISC"
+    },
+    "node_modules/yocto-queue": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    }
+  }
+}
diff --git a/frontend/package.json b/frontend/package.json
new file mode 100644
index 000000000..1c4d8ffc4
--- /dev/null
+++ b/frontend/package.json
@@ -0,0 +1,29 @@
+{
+  "name": "ai-engineer-challenge-frontend",
+  "version": "0.1.0",
+  "private": true,
+  "scripts": {
+    "dev": "next dev",
+    "build": "next build",
+    "start": "next start",
+    "lint": "next lint"
+  },
+  "dependencies": {
+    "autoprefixer": "^10.0.1",
+    "lucide-react": "^0.294.0",
+    "next": "^14.2.32",
+    "openai": "^4.0.0",
+    "postcss": "^8",
+    "react": "^18",
+    "react-dom": "^18",
+    "tailwindcss": "^3.3.0"
+  },
+  "devDependencies": {
+    "@types/node": "^20",
+    "@types/react": "^18",
+    "@types/react-dom": "^18",
+    "eslint": "^8",
+    "eslint-config-next": "14.0.4",
+    "typescript": "^5"
+  }
+}
diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js
new file mode 100644
index 000000000..0cc9a9ded
--- /dev/null
+++ b/frontend/postcss.config.js
@@ -0,0 +1,6 @@
+module.exports = {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+} 
\ No newline at end of file
diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js
new file mode 100644
index 000000000..0b44eb65b
--- /dev/null
+++ b/frontend/tailwind.config.js
@@ -0,0 +1,84 @@
+/** @type {import('tailwindcss').Config} */
+module.exports = {
+  content: [
+    './pages/**/*.{js,ts,jsx,tsx,mdx}',
+    './components/**/*.{js,ts,jsx,tsx,mdx}',
+    './app/**/*.{js,ts,jsx,tsx,mdx}',
+  ],
+  theme: {
+    extend: {
+      colors: {
+        // Cookie-inspired warm palette
+        cookie: {
+          50: '#fef7ed',
+          100: '#fdedd3',
+          200: '#fbd9a5',
+          300: '#f8c16d',
+          400: '#f5a33a',
+          500: '#f28c0c',
+          600: '#d97706',
+          700: '#b45309',
+          800: '#92400e',
+          900: '#78350f',
+        },
+        // Chocolate browns for depth
+        chocolate: {
+          50: '#faf7f2',
+          100: '#f3ede1',
+          200: '#e6d9c2',
+          300: '#d4c1a0',
+          400: '#c1a67a',
+          500: '#a68b5b',
+          600: '#8b6f47',
+          700: '#6b5538',
+          800: '#4a3c26',
+          900: '#2d2417',
+        },
+        // Deep blacks like Cursor
+        dark: {
+          50: '#f8fafc',
+          100: '#f1f5f9',
+          200: '#e2e8f0',
+          300: '#cbd5e1',
+          400: '#94a3b8',
+          500: '#64748b',
+          600: '#475569',
+          700: '#334155',
+          800: '#1e293b',
+          900: '#0f172a',
+          950: '#020617',
+        },
+        // Pure black for Cursor-like interface
+        black: {
+          50: '#f6f6f6',
+          100: '#e7e7e7',
+          200: '#d1d1d1',
+          300: '#b0b0b0',
+          400: '#888888',
+          500: '#6d6d6d',
+          600: '#5d5d5d',
+          700: '#4f4f4f',
+          800: '#454545',
+          900: '#3d3d3d',
+          950: '#1a1a1a',
+        }
+      },
+      animation: {
+        'fade-in': 'fadeIn 0.5s ease-in-out',
+        'slide-up': 'slideUp 0.3s ease-out',
+        'pulse-slow': 'pulse 3s cubic-bezier(0.4, 0, 0.6, 1) infinite',
+      },
+      keyframes: {
+        fadeIn: {
+          '0%': { opacity: '0' },
+          '100%': { opacity: '1' },
+        },
+        slideUp: {
+          '0%': { transform: 'translateY(10px)', opacity: '0' },
+          '100%': { transform: 'translateY(0)', opacity: '1' },
+        },
+      },
+    },
+  },
+  plugins: [],
+} 
\ No newline at end of file
diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json
new file mode 100644
index 000000000..302c4cf02
--- /dev/null
+++ b/frontend/tsconfig.json
@@ -0,0 +1,27 @@
+{
+  "compilerOptions": {
+    "target": "es5",
+    "lib": ["dom", "dom.iterable", "es6"],
+    "allowJs": true,
+    "skipLibCheck": true,
+    "strict": true,
+    "noEmit": true,
+    "esModuleInterop": true,
+    "module": "esnext",
+    "moduleResolution": "bundler",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "jsx": "preserve",
+    "incremental": true,
+    "plugins": [
+      {
+        "name": "next"
+      }
+    ],
+    "paths": {
+      "@/*": ["./*"]
+    }
+  },
+  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
+  "exclude": ["node_modules"]
+} 
\ No newline at end of file