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\u001b[0mall_texts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;31m# Embed texts using MPNet sentence transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'text'" ] } ], "source": [ "import os\n", "!pip install transformers datasets faiss-gpu sentence_transformers\n", "os.environ[\"HUGGING_FACE_HUB_TOKEN\"] = \"your_token_here\"\n", "\n", "\n", "import torch\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "from datasets import load_dataset\n", "from sentence_transformers import SentenceTransformer\n", "import faiss\n", "import numpy as np\n", "\n", "# Load Mistral 7B model and tokenizer\n", "model_name = \"mistralai/Mistral-7B-v0.1\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)\n", "if torch.cuda.is_available():\n", " model.cuda()\n", "\n", "# Load datasets\n", "datasets = [\n", " load_dataset(\"fredmo/hr_rules_dataset\", split=\"train\"),\n", " load_dataset(\"fredmo/vertexai-qna-50\", split=\"train\"),\n", " load_dataset(\"fredmo/dc_rules_dataset\", split=\"train\")\n", "]\n", "\n" ] }, { "cell_type": "code", "source": [ "# Concatenate all datasets into a single list of inputs and outputs\n", "all_inputs = []\n", "all_outputs = []\n", "\n", "for dataset in datasets:\n", " if dataset.column_names == [\"input\", \"output\"]: # fredmo/hr_rules_dataset, fredmo/dc_rules_dataset\n", " for example in dataset:\n", " all_inputs.append(example[\"input\"])\n", " all_outputs.append(example[\"output\"])\n", " elif dataset.column_names == [\"input_text\", \"output_text\"]: # fredmo/vertexai-qna-50\n", " for example in dataset:\n", " all_inputs.append(example[\"input_text\"])\n", " all_outputs.append(example[\"output_text\"])\n", " else:\n", " print(f\"Unexpected column names: {dataset.column_names}\")\n", "\n", "\n", "# Embed inputs using MPNet sentence transformer with chunking\n", "encoder = SentenceTransformer('all-mpnet-base-v2')\n", "input_embeddings = []\n", "chunk_size = 512 # Adjust this based on your available memory\n", "\n", "for i in range(0, len(all_inputs), chunk_size):\n", " chunk = all_inputs[i:i+chunk_size]\n", " chunk_embeddings = encoder.encode(chunk, show_progress_bar=True)\n", " input_embeddings.extend(chunk_embeddings)\n", "\n", "input_embeddings = np.array(input_embeddings)\n", "\n", "# Create FAISS index for input embeddings\n", "dimension = input_embeddings.shape[1]\n", "index = faiss.IndexFlatL2(dimension)\n", "index.add(input_embeddings)\n", "\n", "\n", "# Example usage\n", "query = \"What are the rules for vacation time?\"\n", "results = search(query)\n", "for result in results:\n", " print(result)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 457, "referenced_widgets": [ "9621a23eda984cd29b1ab998890dfe4d", "5476f6baf04b4332b6e2dd5c4b678bc5", "6328d2f6c827465a8f26a93371d5eb3e", "1ac56170b14c483a8300c2ce89cdb783", "92906308b9bd460c857c9966c4cf8550", "9d1b76b628f84fea91389a4d1907b3ef", "1e407098a65546588c74718d1e0b3b52", "3b6311ae14854c82b5444ffb67ad1281", "47a440308d744b68868a29deb0766da0", "fb5a9e867ddb49f3b66b75ff6f9fe800", "d5816c2ee69343498891a8fab541b00d", "d7a2aeeaa56645f6b6104965756830e5", "f40fa9ff12824389b5082f79f01451da", "2d50f5b3e44e48b2a353ff9f13cedcfb", "828807a3c4db4e1389e093e5950e3d14", "87c4bc93e3f4495a8c343614e0cfae9a", "7247fd3b255f48ff9696251bc751301c", "258251b3a63043fc890660c8389e53ae", "44932a6d08c34f3a8cfe01cd477eafb1", "0a0bd6e61f97436493cf8da327715ba9", "a5ef09d9e8b64091a5e0643d04abbd61", "7df712e012b64d20b7cc1c38a26bfe10", "cd6022baf7ee48eabfdcfc9c13174bac", "033ea99f783a4b7eb3efe022b887f437", "bf85674842cc46c683c480934dab0752", "278c644c685044bc90b63489224ed342", "74a0bdb0a0444bb0ab6e95a6c4a665f5", "d2657989832045a4b89b11fe79ddfe9d", "e5383eda6492423fa05a70a02c9ee5b6", "da7ad04a56f94785a35aaaf92263b94a", "a1fea1bd85c4476d82711ff029d94c77", "9e2c0a245b6249469c7de80e51b651a2", "64c1fc26301546f6ad6c658c3660000d", "048eae76c8b64c3887712e9e6c484d94", "b108f8aa8a484a3d8b07b9415ef7caa2", "31b00c825b3b4ec2a2958a5e661f76a2", "c740ceffa1084c37824d858446119e82", "7dabaf3a64774506a14d2f95d90271ad", "46cdf67c97124fcf8ea8b31f72d0246c", "2c2f5f3c909b4269b668014883648161", "b0adfa8a884946bdbec4fad98d988d31", "3c9f8d1f4575446ab4269ec33f8336e5", "722190e349454911911beca24038b835", "544f92f690bb40bdac12e0b798f83e9c", "69a988ccc1794b638b489aeee9384ec8", "51f7ac4618014610941ae57e3281a5eb", "da77915ff8884b709a72af9dfef640b5", "fb7fa8f48f4a45aca55f57a25831ba7b", "e567db30453f42929ccaa50dcdd5f273", "a6eb091dc1564e15b520c1b1cde90674", "eddb8af3693c41df85d742b6433e0403", "d6c14150b6c94d339d0ce25e0cc75037", "945a8582c58148b3b40fcf661e4c53e7", "c1bde5caadb744b88b7e51794730436c", "03ace7b9387449ae8991b086d61eeb9b", "e10000ef08384e0c843c0d8cf50ad918", "8812ca2f81d24df488fbb5ac7fe8c8de", "a4045694c8dc4e60a128a0bf94511f50", "2ca933b5ab1b4c2d95af0ead7a7ff738", "2197826b55634643853beb6735b8b5ba", "b6323cd429004bcbbf1261e18ee362f3", "fcf4219aafb9429b96442e603891652e", "27d536bf814444ceb969bbfb94c08b87", "b4b5e5d1d0ee4f2193a432ec22f454a4", "36d0a826edfb40feac133763bb7772c3", "87bbd9cc56ad4e4097d20364fd83147c", "f4e34098fe7b4084b87fa9037d09828b", "1e9c143221fc4209afa2a4d708abfb23", "3ca3f79f575c4da5b9d626dd903441cd", "4a6a1d03d0094780a8d01ae6a726c6aa", "4eed39891d664dcb8694f38fa9371833", "0f9964edd4f64863aaa5da694847c60d", "112b823ff1dc4307a09ab23885f7775c", "3e677591fc13492abf8b6e9a88ea66f8", "a52ebf9d772b4c728baf302978d17f28", "5b4ddae258d547a2b1211a664a48fc81", 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"5d93105e17df4e90a12a00d07783a30d", "e1af28983b30425aae7372a0e171a545" ] }, "id": "11yaCk8-N_Z1", "outputId": "f1caf9ca-a764-40c6-ec38-631ce4248a6e" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/349 [00:00 best_score:\n", " best_score = score\n", " best_index = i\n", "\n", " selected_indices.append(best_index)\n", " remaining_indices.remove(best_index)\n", "\n", " return selected_indices\n", "\n", "# Function to search for similar inputs with MMR diversification\n", "def search_with_mmr(query, k=17, top_k=10, diversity=0.8):\n", " query_embedding = encoder.encode([query])[0]\n", " D, I = index.search(np.array([query_embedding]), k) # Retrieve k=17 results\n", "\n", " # Apply MMR diversification\n", " mmr_indices = mmr(query_embedding, input_embeddings[I[0]], top_k, diversity)\n", "\n", " # Print retrieved chunks and outputs\n", " for i in mmr_indices:\n", " print (\"Retreived with MMR:\")\n", " idx = I[0][i] # Get the original index from FAISS results\n", " print(f\"Input: {all_inputs[idx]}\")\n", " print(f\"Output: {all_outputs[idx]}\")\n", " print(\"-\" * 20)\n", "\n", " return [all_outputs[I[0][i]] for i in mmr_indices]\n", "\n", "# Example usage\n", "query = \"What is intelligence in Google's security ?\"\n", "results = search_with_mmr(query, k=17, top_k=3, diversity=0.8)\n", "print(\"Mistral 7B LLM Output \")\n", "for result in results:\n", " print(result)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zcLPohZsQwaZ", "outputId": "4786101c-f8bc-4e8e-8a6a-af261ca4f173" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Retreived with MMR (close and diffrent from each others):\n", "Input: What is the role of artificial intelligence in Google's security strategy?\n", "Output: AI plays a significant role in Google's security strategy by automating threat detection, improving incident response, and enhancing security analysis. For example, AI can be used to identify suspicious activity, detect malware, and analyze security logs.\n", "--------------------\n", "Retreived with MMR (close and diffrent from each others):\n", "Input: What is the Threat Analysis Group at Google?\n", "Output: The Threat Analysis Group monitors threat actors, their tactics, and techniques, improving the safety and security of Google products and sharing intelligence with the online community.\n", "--------------------\n", "Retreived with MMR (close and diffrent from each others):\n", "Input: How does Google measure and improve the effectiveness of its security mechanisms?\n", "Output: Google conducts Red Team exercises to simulate attacks and evaluate the effectiveness of its detection and response mechanisms.\n", "--------------------\n", "Mistral 7B LLM Output \n", "AI plays a significant role in Google's security strategy by automating threat detection, improving incident response, and enhancing security analysis. For example, AI can be used to identify suspicious activity, detect malware, and analyze security logs.\n", "The Threat Analysis Group monitors threat actors, their tactics, and techniques, improving the safety and security of Google products and sharing intelligence with the online community.\n", "Google conducts Red Team exercises to simulate attacks and evaluate the effectiveness of its detection and response mechanisms.\n" ] } ] } ] }