10.RAG-Using-Mistral-LangChain-FAISS.ipynb

{
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    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_kg_hide-output": true,
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
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     "start_time": "2024-02-01T10:11:12.098027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### RAG using Mistral, LangChain and FAISS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f4abd5e",
   "metadata": {
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     "exception": false,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import transformers\n",
    "\n",
    "from glob import glob\n",
    "from tqdm.notebook import tqdm\n",
    "from transformers import AutoTokenizer,AutoModelForCausalLM,BitsAndBytesConfig,pipeline\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.document_loaders import PyPDFLoader\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
    "from langchain.vectorstores import FAISS\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.schema.runnable import RunnablePassthrough\n",
    "from langchain.llms import HuggingFacePipeline\n",
    "from langchain.chains import LLMChain\n",
    "from langchain.schema.output_parser import StrOutputParser\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2d0b5db",
   "metadata": {
    "papermill": {
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     "exception": false,
     "start_time": "2024-02-01T10:12:11.135043",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU is available.\n",
      "Using GPU:  NVIDIA A100-SXM4-40GB\n",
      "Memory Usage: \n",
      "Allocated:  0.0 GB\n",
      "Cached:  0.0 GB\n"
     ]
    }
   ],
   "source": [
    "# Checking if GPU is available\n",
    "if torch.cuda.is_available():\n",
    "    print(\"GPU is available.\")\n",
    "    print('Using GPU: ', torch.cuda.get_device_name(0))\n",
    "    print('Memory Usage: ')\n",
    "    print('Allocated: ', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')\n",
    "    print('Cached: ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB')\n",
    "\n",
    "else:\n",
    "    print(\"GPU is not available.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "378fbb52-cd4c-4dc6-8f11-2ba5d5937421",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = f'cuda:{torch.cuda.current_device()}' if torch.cuda.is_available() else 'cpu'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f164d53",
   "metadata": {
    "papermill": {
     "duration": 0.006218,
     "end_time": "2024-02-01T10:12:11.232893",
     "exception": false,
     "start_time": "2024-02-01T10:12:11.226675",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Setting bitsandbytes config to improve speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "809d93b5",
   "metadata": {
    "papermill": {
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     "end_time": "2024-02-01T10:12:11.254717",
     "exception": false,
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    "tags": []
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   "outputs": [],
   "source": [
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=\"float16\",\n",
    "    bnb_4bit_use_double_quant=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "754eb1fe",
   "metadata": {
    "papermill": {
     "duration": 0.005972,
     "end_time": "2024-02-01T10:12:11.266914",
     "exception": false,
     "start_time": "2024-02-01T10:12:11.260942",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Loading Mistral-7B Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39a6f8ac",
   "metadata": {
    "papermill": {
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     "exception": false,
     "start_time": "2024-02-01T10:12:11.272952",
     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0255c9a9b7dd4edbb8b71247218e4eb8",
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       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_id = \"../pretrained_models/Mistral-7B-Instruct-v0.2\"\n",
    "model = AutoModelForCausalLM.from_pretrained(model_id,quantization_config=bnb_config,do_sample=True,device_map = \"auto\")\n",
    "\n",
    "tokenizer = tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3d479a",
   "metadata": {
    "papermill": {
     "duration": 0.006422,
     "end_time": "2024-02-01T10:14:22.900904",
     "exception": false,
     "start_time": "2024-02-01T10:14:22.894482",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Testing model response without RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a2fef0a1",
   "metadata": {
    "papermill": {
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s> Tell me about Transformers.\n",
      "\n",
      "Transformers is a line of toy robots created by Hasbro and released in 1984. These toys can transform from one form into another. The most popular Transformers are the Autobots and Decepticons, two factions engaged in an endless battle. Each Transformer is represented by distinctive vehicles and robots in both modes.\n",
      "\n",
      "The Transformers originated from a Japanese toy line called Diaclone, which featured robots that combined into larger vehicles and back again. Hasbro acquired the distribution rights to these toys but wanted a more distinctive theme for the product. They partnered with Marvel Comics to create the backstory and narrative surrounding the Transformers.\n",
      "\n",
      "The characters from Transformers have since been adapted into various forms of media. These include cartoons, films, comic books, video games, and novels. The story revolves around the war between the Autobots, led by Optimus Prime, and the Decepticons, led by Megatron, for control over the AllSpark, a power source that can create new Transformers.\n",
      "\n",
      "Transformers have been a cultural phenomenon for decades and continue to inspire fan art, cosplay, merchandise, and new generations of fans.\n",
      "\n",
      "What is the difference between Autobots and Decepticons?\n",
      "\n",
      "Autobots and Decepticons are two primary factions within the Transformers universe. The Autobots are\n"
     ]
    }
   ],
   "source": [
    "prompt = \"Tell me about Transformers.\"\n",
    "\n",
    "model_inputs = tokenizer([prompt], return_tensors=\"pt\").to(device)\n",
    "\n",
    "generated_ids = model.generate(**model_inputs, max_new_tokens=300, do_sample=True,pad_token_id=tokenizer.eos_token_id)\n",
    "decoded = tokenizer.batch_decode(generated_ids)[0]\n",
    "\n",
    "print(decoded.replace('\\\\n', '\\n'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e6364f",
   "metadata": {
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     "start_time": "2024-02-01T10:14:43.410425",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Loading supplementary data"
   ]
  },
  {
   "cell_type": "code",
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      },
      "text/plain": [
       "Processing PDFs:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Created a chunk of size 944, which is longer than the specified 500\n",
      "Created a chunk of size 714, which is longer than the specified 500\n",
      "Created a chunk of size 1401, which is longer than the specified 500\n",
      "Created a chunk of size 1469, which is longer than the specified 500\n",
      "Created a chunk of size 1742, which is longer than the specified 500\n",
      "Created a chunk of size 2142, which is longer than the specified 500\n",
      "Multiple definitions in dictionary at byte 0xc1b for key /F16\n"
     ]
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   ],
   "source": [
    "paper_paths = glob(\"dataset/100-llm-papers-to-explore/*.pdf\")\n",
    "pages = []\n",
    "\n",
    "# Initialize the progress bar\n",
    "progress_bar = tqdm(total=len(paper_paths), desc=\"Processing PDFs\")\n",
    "\n",
    "for path in paper_paths:\n",
    "    try:\n",
    "        loader = PyPDFLoader(path)\n",
    "        doc = loader.load()\n",
    "        \n",
    "        # Chunk text\n",
    "        text_splitter = CharacterTextSplitter(chunk_size=500, \n",
    "                                              chunk_overlap=0)\n",
    "        chunked_documents = text_splitter.split_documents(doc)\n",
    "        \n",
    "        pages.extend(chunked_documents)\n",
    "    except Exception as e:\n",
    "        print(f'Skipping {path} due to error: {e}')\n",
    "    \n",
    "    # Update the progress bar\n",
    "    progress_bar.update(1)\n",
    "\n",
    "# Close the progress bar\n",
    "progress_bar.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b325112",
   "metadata": {
    "papermill": {
     "duration": 0.007008,
     "end_time": "2024-02-01T10:20:29.279150",
     "exception": false,
     "start_time": "2024-02-01T10:20:29.272142",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Creating a RAG Using LangChain and FAISS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3846b32",
   "metadata": {
    "papermill": {
     "duration": 0.016843,
     "end_time": "2024-02-01T10:20:29.303047",
     "exception": false,
     "start_time": "2024-02-01T10:20:29.286204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "text_generation_pipeline = pipeline(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    device_map=\"auto\",\n",
    "    task=\"text-generation\",\n",
    "    temperature=0.2,\n",
    "    repetition_penalty=1.1,\n",
    "    return_full_text=True,\n",
    "    max_new_tokens=300,\n",
    "    eos_token_id=tokenizer.eos_token_id,\n",
    ")\n",
    "\n",
    "mistral_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fadd027f",
   "metadata": {
    "papermill": {
     "duration": 0.015372,
     "end_time": "2024-02-01T10:20:29.325229",
     "exception": false,
     "start_time": "2024-02-01T10:20:29.309857",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "prompt_template = \"\"\"\n",
    "Instruction: Answer the question based on the following context:\n",
    "{context}\n",
    "\n",
    "Question:\n",
    "{question} \n",
    " \"\"\"\n",
    "\n",
    "# Create prompt from prompt template \n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"context\", \"question\"],\n",
    "    template=prompt_template,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f54aa73",
   "metadata": {
    "papermill": {
     "duration": 0.014075,
     "end_time": "2024-02-01T10:20:29.346142",
     "exception": false,
     "start_time": "2024-02-01T10:20:29.332067",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create llm chain \n",
    "llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "02d95ab1",
   "metadata": {
    "papermill": {
     "duration": 47.81547,
     "end_time": "2024-02-01T10:21:17.168271",
     "exception": false,
     "start_time": "2024-02-01T10:20:29.352801",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load chunked documents into the FAISS index with sentence-transformers/all-mpnet-base-v2\n",
    "embedding_model_name = \"/home/jomondal/experiments/mywork/pretrained_models/all-MiniLM-L6-v2\"\n",
    "db = FAISS.from_documents(\n",
    "    pages,\n",
    "    HuggingFaceEmbeddings(model_name=embedding_model_name)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ac9ded",
   "metadata": {
    "papermill": {
     "duration": 0.010995,
     "end_time": "2024-02-01T10:21:17.192789",
     "exception": false,
     "start_time": "2024-02-01T10:21:17.181794",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Creating a RAG Chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59c681a5",
   "metadata": {
    "papermill": {
     "duration": 0.022957,
     "end_time": "2024-02-01T10:21:17.230250",
     "exception": false,
     "start_time": "2024-02-01T10:21:17.207293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Connect query to FAISS index using a retriever\n",
    "retriever = db.as_retriever(\n",
    "    search_type=\"similarity\",\n",
    "    search_kwargs={'k': 4}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67c98ba9",
   "metadata": {
    "papermill": {
     "duration": 0.020962,
     "end_time": "2024-02-01T10:21:17.263416",
     "exception": false,
     "start_time": "2024-02-01T10:21:17.242454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rag_chain = ( \n",
    " {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | llm_chain\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d313d38",
   "metadata": {
    "papermill": {
     "duration": 0.011891,
     "end_time": "2024-02-01T10:21:17.341716",
     "exception": false,
     "start_time": "2024-02-01T10:21:17.329825",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Testing model response with RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c53e38d6",
   "metadata": {
    "papermill": {
     "duration": 12.982637,
     "end_time": "2024-02-01T10:21:30.336248",
     "exception": false,
     "start_time": "2024-02-01T10:21:17.353611",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
     ]
    }
   ],
   "source": [
    "query = \"Tell me about Transformers.\"\n",
    "response = rag_chain.invoke(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64727ad2",
   "metadata": {
    "papermill": {
     "duration": 0.018803,
     "end_time": "2024-02-01T10:21:30.365822",
     "exception": false,
     "start_time": "2024-02-01T10:21:30.347019",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question: Tell me about Transformers.\n",
      "\n",
      "Answer:\n",
      "Transformers are a type of neural network architecture introduced in the paper \"Attention Is All You Need\" by Vaswani et al. (2017). They have since become widely used in natural language processing tasks due to their ability to handle long-range dependencies in sequences. Transformers use self-attention mechanisms to allow each token in a sequence to attend to all other tokens, enabling the model to focus on relevant information regardless of its position in the sequence. This is in contrast to traditional recurrent neural networks, which process sequences one token at a time and can struggle with long-term dependencies.\n",
      "\n",
      "Since their introduction, there have been many variations and improvements to the Transformer architecture. Some notable works include RoFormer (Devlin et al., 2017), BERT (Devlin et al., 2019), Longformer (Beltagy et al., 2020), and T5 (Raffel et al., 2020). These models have achieved state-of-the-art results on various natural language processing tasks such as language modeling, text classification, and machine translation.\n",
      "\n",
      "More recent developments in Transformers include efficient attention mechanisms (Shen et al., 2021; Zuo et al., 2022), attention-free Transformers (Zhai et al., 2021), and adapt\n"
     ]
    }
   ],
   "source": [
    "print (\"Question:\", response[\"question\"])\n",
    "print (response[\"text\"].replace('\\\\n', '\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf3436f6-891d-41f1-9244-2ac56760f514",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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