From d8ed974775710b1ed09a416fd7dbf0dce868f068 Mon Sep 17 00:00:00 2001 From: Kevin Thomas Date: Fri, 2 Aug 2024 17:46:45 -0400 Subject: [PATCH] Add files via upload --- rea.ipynb | 5690 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 5690 insertions(+) create mode 100644 rea.ipynb diff --git a/rea.ipynb b/rea.ipynb new file mode 100644 index 0000000..6711f5b --- /dev/null +++ b/rea.ipynb @@ -0,0 +1,5690 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reverse Engineering Assistant\n", + "Reverse Engineering Assistant utilizing RAG and an LLM." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retrieval-Augmented Generation (RAG) is a powerful technique in natural language processing (NLP) that combines retrieval-based methods with generative models to produce more accurate and contextually relevant outputs. This approach was introduced in the 2020 paper \"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\" by Facebook AI Research (FAIR)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For further reading and a deeper understanding of RAG, refer to the original paper by Facebook AI Research: [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview of the RAG Architecture\n", + "\n", + "The RAG model consists of two main components:\n", + "1. **Retriever**: This component retrieves relevant documents from a large corpus based on the input query.\n", + "2. **Generator**: This component generates responses conditioned on the retrieved documents.\n", + "\n", + "## Mathematical Formulation\n", + "\n", + "### Retriever\n", + "\n", + "The retriever selects the top $k$ documents from a corpus $\\mathcal{D}$ based on their relevance to the input query $q$.\n", + "\n", + "### Generator\n", + "\n", + "The generator produces a response $r$ based on the input query $q$ and the retrieved documents $\\{d_1, d_2, \\ldots, d_k\\}$.\n", + "\n", + "### Combining Retriever and Generator\n", + "\n", + "The final probability of generating a response $r$ given the query $q$ is obtained by marginalizing over the top $k$ retrieved documents:\n", + "\n", + "$$\n", + "P(r \\mid q) = \\sum_{i=1}^{k} P(d_i \\mid q) P(r \\mid q, d_i)\n", + "$$\n", + "\n", + "Here, $P(d_i \\mid q)$ is the normalized relevance score of document $d_i$ given the query $q$, and $P(r \\mid q, d_i)$ is the probability of generating response $r$ given the query $q$ and document $d_i$.\n", + "\n", + "## Implementation Details\n", + "\n", + "### Training\n", + "\n", + "The RAG model is trained in two stages:\n", + "1. **Retriever Training**: The retriever is trained to maximize the relevance score $s(q, d_i)$ for relevant documents.\n", + "2. **Generator Training**: The generator is trained to maximize the probability $P(r \\mid q, d_i)$ for the ground-truth responses.\n", + "\n", + "### Inference\n", + "\n", + "During inference, the RAG model retrieves the top $k$ documents for a given query and generates a response conditioned on these documents. The final response is obtained by marginalizing over the retrieved documents as described above.\n", + "\n", + "## Conclusion\n", + "\n", + "RAG leverages the strengths of both retrieval-based and generation-based models to produce more accurate and informative responses. By conditioning the generation on retrieved documents, RAG can incorporate external knowledge from large corpora, leading to better performance on various tasks.\n", + "\n", + "The combination of retriever and generator in the RAG model makes it a powerful approach for tasks that require access to external knowledge and the ability to generate coherent and contextually appropriate responses." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DfRE18X_mqvI" + }, + "source": [ + "### Install Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qfBKaUU2mqvK", + "outputId": "b1f5db4e-9d91-4f9f-e2ba-15e75edb0caa" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: llama-index==0.10.18 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (0.10.18)\n", + "Requirement already satisfied: llama-index-llms-groq==0.1.3 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already satisfied: mypy-extensions>=0.3.0 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from typing-inspect>=0.8.0->llama-index-core<0.11.0,>=0.10.18->llama-index==0.10.18) (1.0.0)\n", + "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from dataclasses-json->llama-index-core<0.11.0,>=0.10.18->llama-index==0.10.18) (3.21.3)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from pandas->llama-index-core<0.11.0,>=0.10.18->llama-index==0.10.18) (2.9.0)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from pandas->llama-index-core<0.11.0,>=0.10.18->llama-index==0.10.18) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from pandas->llama-index-core<0.11.0,>=0.10.18->llama-index==0.10.18) (2024.1)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from scikit-learn->sentence-transformers<3.0.0,>=2.6.1->llama-index-embeddings-huggingface==0.2.0) (3.5.0)\n", + "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas->llama-index-core<0.11.0,>=0.10.18->llama-index==0.10.18) (1.16.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from jinja2->torch>=1.11.0->sentence-transformers<3.0.0,>=2.6.1->llama-index-embeddings-huggingface==0.2.0) (2.1.5)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/anaconda3/envs/rea/lib/python3.11/site-packages (from sympy->torch>=1.11.0->sentence-transformers<3.0.0,>=2.6.1->llama-index-embeddings-huggingface==0.2.0) (1.3.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install llama-index==0.10.18 llama-index-llms-groq==0.1.3 groq==0.4.2 llama-index-embeddings-huggingface==0.2.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dO7FZrPBmqvK" + }, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "WDBGD1-EmqvK" + }, + "outputs": [], + "source": [ + "from llama_index.core import (\n", + " VectorStoreIndex,\n", + " SimpleDirectoryReader,\n", + " StorageContext,\n", + " ServiceContext,\n", + " load_index_from_storage\n", + ")\n", + "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n", + "from llama_index.core.node_parser import SentenceSplitter\n", + "from llama_index.llms.groq import Groq\n", + "# import os\n", + "# from dotenv import load_dotenv\n", + "# load_dotenv()\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ACTION ITEM\n", + "Visit https://console.groq.com/keys and set up an API Key then replace `` below with the newly generated key." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "LvvCfO7qmqvL" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"GROQ_API_KEY\"] = \"\"\n", + "GROQ_API_KEY = os.getenv(\"GROQ_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7nsQejq6mqvL" + }, + "source": [ + "### Data Ingestion" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "DLZlByiMmqvL" + }, + "outputs": [], + "source": [ + "reader = SimpleDirectoryReader(input_files=[\"files/reversing-for-everyone.pdf\"])\n", + "documents = reader.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "btyQtziemqvL", + "outputId": "ac9934a0-fc96-4ea2-81ee-155fdaa6ebba" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "430" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ByzUMWVBmqvM", + "outputId": "c949257a-784e-4095-953f-06fbbc7b737d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'page_label': '5',\n", + " 'file_name': 'reversing-for-everyone.pdf',\n", + " 'file_path': 'files/reversing-for-everyone.pdf',\n", + " 'file_type': 'application/pdf',\n", + " 'file_size': 25112355,\n", + " 'creation_date': '2024-08-02',\n", + " 'last_modified_date': '2024-08-02'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "documents[4].metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kK3fmpYxmqvM" + }, + "source": [ + "### Chunking" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + 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"execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + } + ], + "source": [ + "import gradio as gr\n", + "\n", + "\n", + "def query_function(query):\n", + " \"\"\"\n", + " Processes a query using the query engine and returns the response.\n", + "\n", + " Args:\n", + " query (str): The query string to be processed by the query engine.\n", + "\n", + " Returns:\n", + " str: The response generated by the query engine based on the input query.\n", + " \n", + " Example:\n", + " >>> query_function(\"What is Reverse Engineering?\")\n", + " 'Reverse engineering is the process of deconstructing an object to understand its design, architecture, and functionality.'\n", + " \"\"\"\n", + " response = query_engine.query(query)\n", + " return response\n", + "\n", + "\n", + "iface = gr.Interface(\n", + " fn=query_function, # Function to call\n", + " inputs=gr.Textbox(label=\"Query\"), # Input component\n", + " outputs=gr.Textbox(label=\"Response\") # Output component\n", + ")\n", + "\n", + "iface.launch()" + ] + } + ], + "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": "3.11.9" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0084f7a391214c3f925bb8b6f6ea8153": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": 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