AmritaGPT_RAG_final.ipynb

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  "cells": [
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        "!pip install langchain bitsandbytes accelerate\n",
        "!pip install -q -U faiss-cpu tiktoken sentence-transformers\n",
        "!pip install langchain-google-genai"
      ],
      "metadata": {
        "id": "w6-LkedPSVUu"
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    },
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      "source": [
        "!pip install FastAPI pydantic uvicorn pyngrok"
      ],
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      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "from huggingface_hub import notebook_login\n",
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      "execution_count": null,
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        {
          "output_type": "display_data",
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            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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    },
    {
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      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.embeddings import CacheBackedEmbeddings, HuggingFaceEmbeddings\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "from langchain.storage import LocalFileStore\n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.callbacks import StdOutCallbackHandler\n",
        "import torch\n",
        "import transformers"
      ],
      "metadata": {
        "id": "pG-_jHZHVRnf"
      },
      "execution_count": null,
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    },
    {
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        "with open(\"general.txt\", encoding=\"utf8\") as f:\n",
        "    raw_text = f.read()\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size = 1000, # the character length of the chunk\n",
        "    chunk_overlap = 100, # the character length of the overlap between chunks\n",
        "    length_function = len, # the length function - in this case, character length (aka the python len() fn.)\n",
        ")\n",
        "\n",
        "combined_documents = text_splitter.split_text(raw_text)\n",
        "\n",
        "store = LocalFileStore(\"./cache/\")\n",
        "embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'\n",
        "core_embeddings_model = HuggingFaceEmbeddings(model_name=embed_model_id)\n",
        "embedder = CacheBackedEmbeddings.from_bytes_store(\n",
        "    core_embeddings_model, store, namespace=embed_model_id)\n",
        "vector_store = FAISS.from_texts(combined_documents, embedder)"
      ],
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        "id": "alDZclzrSNS4",
        "outputId": "52071e0b-5ce5-42ad-db62-e4072a1375b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
            "  return self.fget.__get__(instance, owner)()\n"
          ]
        },
        {
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        {
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        {
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            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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    },
    {
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      "source": [
        "model_id = \"meta-llama/Llama-2-13b-chat-hf\"\n",
        "\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "model_config = transformers.AutoConfig.from_pretrained(model_id)\n",
        "\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    config=model_config,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map='auto'\n",
        ")\n",
        "\n",
        "model.eval()\n",
        "\n",
        "tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)\n",
        "generate_text = transformers.pipeline(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    task=\"text-generation\",\n",
        "    return_full_text=True,\n",
        "    temperature=0.7,\n",
        "    max_new_tokens=256\n",
        ")\n",
        "llm = HuggingFacePipeline(pipeline=generate_text)\n",
        "retriever = vector_store.as_retriever()\n",
        "\n",
        "handler = StdOutCallbackHandler()\n",
        "\n",
        "qa_with_sources_chain = RetrievalQA.from_chain_type(\n",
        "      llm=llm,\n",
        "      retriever=retriever,\n",
        "      callbacks=[handler],\n",
        "      return_source_documents=True\n",
        "  )"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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    },
    {
      "cell_type": "code",
      "source": [
        "def generate_response(question):\n",
        "  return qa_with_sources_chain({\"query\" : question})['result']"
      ],
      "metadata": {
        "id": "SKN6HfhMWYOy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_response(\"When did the Coimbatore campus start ?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "3npp2iNHTzQl",
        "outputId": "c8d3a397-fce1-439a-a5cc-8aa8f75903b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
            "  warn_deprecated(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' The Coimbatore campus started in 1994.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **FASTAPI**"
      ],
      "metadata": {
        "id": "zZkRSwAKSCME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "sPmOWMqBYF9Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ngrok config add-authtoken 2byhqoCrlLHUUIkJkBxspq8hs1R_6cPvKVeHHsTTuPwE3WTiw"
      ],
      "metadata": {
        "id": "P7z6uuZ-6Q0g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from fastapi import FastAPI\n",
        "from pydantic import BaseModel\n",
        "import uvicorn\n",
        "from pyngrok import ngrok\n",
        "from fastapi.middleware.cors import CORSMiddleware\n",
        "import nest_asyncio"
      ],
      "metadata": {
        "id": "7zR64dCa4vnJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = FastAPI()\n",
        "\n",
        "@app.get(\"/\")\n",
        "def index():\n",
        "    return {'hello' : 'world'}\n",
        "\n",
        "@app.get('/get-response/{input_text}')\n",
        "def get_student(input_text : str):\n",
        "    return generate_response(input_text)"
      ],
      "metadata": {
        "id": "qssnGJgs5m2Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "origins = [\"*\"]\n",
        "\n",
        "app.add_middleware(\n",
        "    CORSMiddleware,\n",
        "    allow_origins=origins,\n",
        "    allow_credentials=True,\n",
        "    allow_methods=[\"*\"],\n",
        "    allow_headers=[\"*\"],\n",
        ")"
      ],
      "metadata": {
        "id": "eIK5F4lS5Z6Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ngrok_tunnel = ngrok.connect(8000)\n",
        "print('Public URL:', ngrok_tunnel.public_url)\n",
        "nest_asyncio.apply()\n",
        "uvicorn.run(app, port=8000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kyMm24tm6GB5",
        "outputId": "ef835f88-3293-4cc1-ac11-dd54b17e7031"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception in thread Thread-15 (_monitor_process):\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/usr/lib/python3.10/threading.py\", line 953, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pyngrok/process.py\", line 138, in _monitor_process\n",
            "    self._log_line(self.proc.stdout.readline())\n",
            "  File \"/usr/lib/python3.10/encodings/ascii.py\", line 26, in decode\n",
            "    return codecs.ascii_decode(input, self.errors)[0]\n",
            "UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 184: ordinal not in range(128)\n",
            "INFO:     Started server process [1426]\n",
            "INFO:     Waiting for application startup.\n",
            "INFO:     Application startup complete.\n",
            "INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n"
          ]
        }
      ]
    }
  ]
}