easy_rag_mistral.ipynb

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        "# **EASY IMPLEMENTATION OF RETRIEVAL AUGMENTED GENERATION (RAG) WITH LLM MISTRAL 7B**\n",
        "\n",
        "---\n",
        "\n",
        "📎 README\n",
        "\n",
        "This notebook is an easy implementation of retrieval augmented generation (RAG) using Langchain with Hugging Face's model Mistral-7B-Instruct-v0.1 and a FAISS vector store for storing text embeddings, utilizing Sentence Transformers from Hugging Face.\n",
        "\n",
        "About:\n",
        "\n",
        "*   Use FAISS vectore store and Load/Download vector database.\n",
        "*   Choice between downloading the model or entering your Hugging Face user access token.\n",
        "*   Use a GPU to run the Hugging Face model and the FAISS vector store.\n",
        "*   Use Langchain for Q&A on your vectorized PDF database.\n",
        "\n",
        "References:\n",
        "\n",
        "1.  \"[Q/A with LLMs + Harry Potter & Mistral HF API](https://www.kaggle.com/code/acorn8/q-a-with-llms-harry-potter-mistral-hf-api)\".\n",
        "2.   \"[Advanced RAG Implementation on Custom Data Using Hybrid Search, Embed Caching And Mistral-AI](https://medium.aiplanet.com/advanced-rag-implementation-on-custom-data-using-hybrid-search-embed-caching-and-mistral-ai-ce78fdae4ef6)\".\n"
      ],
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      "source": [
        "# @title 1. ✨ Installing dependences.\n",
        "\n",
        "!pip install langchain pypdf faiss-gpu &> /dev/null\n",
        "!pip install InstructorEmbedding sentence_transformers &> /dev/null\n",
        "!pip install -q git+https://github.com/huggingface/transformers &> /dev/null\n",
        "!pip install huggingface_hub -q &> /dev/null\n",
        "!pip install xformers &> /dev/null\n",
        "!pip install -qU auto-gptq optimum &> /dev/null\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import os\n",
        "import glob\n",
        "import textwrap\n",
        "import langchain\n",
        "import torch\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 2. ✍ Making vector database\n",
        "\n",
        "#@markdown Control parameters for vector database\n",
        "\n",
        "class CFG1:\n",
        "\n",
        "    # splitting\n",
        "    split_chunk_size = 800 #@param {type:\"number\"}\n",
        "    split_overlap = 0 #@param {type:\"number\"}\n",
        "\n",
        "    # embeddings\n",
        "    from langchain.storage import LocalFileStore\n",
        "    embeddings_model_repo = 'sentence-transformers/all-MiniLM-L6-v2'\n",
        "\n",
        "    #@markdown Select archives of database\n",
        "    source = \"Make_database_from_pdf\" #@param [\"Make_database_from_pdf\", \"Load_database\"]\n",
        "\n",
        "    #@markdown Select the folder and upload PDF for conversion (Default pdf, data )\n",
        "    # paths\n",
        "    PDFs_path = \"/content/data\" #@param {type:\"string\"}\n",
        "    Embeddings_path =  \"/content/data/huggingface.pdf\" #@param {type:\"string\"}\n",
        "    Persist_directory = \"/content/data\" #@param {type:\"string\"}\n",
        "\n",
        "# ### download embeddings model\n",
        "from InstructorEmbedding import INSTRUCTOR\n",
        "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
        "from langchain.embeddings import CacheBackedEmbeddings,HuggingFaceEmbeddings\n",
        "from langchain.storage import LocalFileStore\n",
        "from langchain.cache import InMemoryCache\n",
        "\n",
        "\n",
        "core_embeddings = HuggingFaceInstructEmbeddings(\n",
        "    model_name = CFG1.embeddings_model_repo,\n",
        "    model_kwargs = {\"device\": \"cuda\"}\n",
        ")\n",
        "\n",
        "store = LocalFileStore(\"./cache/\")\n",
        "embeddings = CacheBackedEmbeddings.from_bytes_store(\n",
        "    core_embeddings,\n",
        "    store,\n",
        "    namespace = CFG1.embeddings_model_repo\n",
        ")\n",
        "\n",
        "if CFG1.source == \"Make_database_from_pdf\":\n",
        "    # loaders\n",
        "    from langchain.document_loaders import PyPDFLoader\n",
        "    from langchain.document_loaders import DirectoryLoader\n",
        "\n",
        "    #Load text.\n",
        "    loader = DirectoryLoader(\n",
        "        CFG1.PDFs_path,\n",
        "        glob=\"./*.pdf\",\n",
        "        loader_cls=PyPDFLoader,\n",
        "        show_progress=True,\n",
        "        use_multithreading=True\n",
        "    )\n",
        "\n",
        "    documents = loader.load()\n",
        "\n",
        "    # Splitter text\n",
        "    from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "    text_splitter = RecursiveCharacterTextSplitter(\n",
        "        chunk_size = CFG1.split_chunk_size,\n",
        "        chunk_overlap = CFG1.split_overlap\n",
        "    )\n",
        "\n",
        "    texts = text_splitter.split_documents(documents)\n",
        "\n",
        "    # ### create embeddings and DB\n",
        "    # vector stores\n",
        "    from langchain.vectorstores import FAISS\n",
        "\n",
        "    vectordb = FAISS.from_documents(\n",
        "        documents = texts,\n",
        "        embedding = embeddings\n",
        "    )\n",
        "\n",
        "    # ### persist vector database\n",
        "    vectordb.save_local(\"faiss_index_hp\")\n",
        "\n",
        "elif CFG1.source == \"Load_database\":\n",
        "    from langchain.vectorstores import FAISS\n",
        "    ### load vector DB embeddings\n",
        "    vectordb = FAISS.load_local(\n",
        "        CFG1.Embeddings_path,\n",
        "        embeddings\n",
        "    )\n"
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          "text": [
            "load INSTRUCTOR_Transformer\n",
            "max_seq_length  512\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1/1 [00:00<00:00,  2.53it/s]\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "# @title 3. 🛠 Setup the model Mistral-7B-Instruct-v0.1\n",
        "\n",
        "# @markdown Select how to get access to the Hugging Face model.\n",
        "type_access = \"Dowload_model\" #@param [\"Load_model_from_API\", \"Dowload_model\"]\n",
        "\n",
        "class CFG2:\n",
        "\n",
        "    model_name = 'mistralai/Mistral-7B-Instruct-v0.1'\n",
        "\n",
        "    # LLMs parameters\n",
        "    temperature = 0.2 # @param {type:\"slider\", min:0, max:1, step:0.1}\n",
        "    top_p = 0.95 # @param {type:\"slider\", min:0, max:1, step:0.1}\n",
        "    top_k = 5 # @param {type:\"slider\", min:0, max:10, step:1}\n",
        "    repetition_penalty = 1.15 #@param {type:\"number\"}\n",
        "    do_sample = True #@param {type:\"boolean\"}\n",
        "    max_new_tokens = 2048 #@param {type:\"number\"}\n",
        "    num_return_sequences=1 #@param {type:\"number\"}\n",
        "\n",
        "    # similar passages\n",
        "    k = 5 # @param {type:\"slider\", min:0, max:100, step:1}\n",
        "\n",
        "if type_access == \"Load_model_from_API\":\n",
        "\n",
        "    # Api huggingface\n",
        "    from langchain.llms import HuggingFaceHub\n",
        "\n",
        "    huggingfacehub_api_token = \"hf_token\"  #@param {type:\"string\"}\n",
        "\n",
        "    llm = HuggingFaceHub(\n",
        "        repo_id = CFG2.model_name,\n",
        "        model_kwargs={\n",
        "            \"max_new_tokens\": CFG2.max_new_tokens,\n",
        "            \"temperature\": CFG2.temperature,\n",
        "            \"top_p\": CFG2.top_p,\n",
        "            \"repetition_penalty\": CFG2.repetition_penalty,\n",
        "            \"do_sample\": CFG2.do_sample,\n",
        "            \"num_return_sequences\": CFG2.num_return_sequences\n",
        "        },\n",
        "        huggingfacehub_api_token = huggingfacehub_api_token\n",
        "    )\n",
        "\n",
        "elif type_access == \"Dowload_model\":\n",
        "\n",
        "    #Download the quantized GPTQ Model\n",
        "\n",
        "    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "    #import torch\n",
        "\n",
        "    #DEVICE = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "\n",
        "    model_name_or_path = \"TheBloke/Mistral-7B-Instruct-v0.1-GPTQ\"\n",
        "    # To use a different branch, change revision\n",
        "    # For example: revision=\"gptq-4bit-32g-actorder_True\"\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_name_or_path,\n",
        "                                                device_map=\"auto\",\n",
        "                                                trust_remote_code=False,\n",
        "                                                revision=\"gptq-4bit-32g-actorder_True\")\n",
        "    #\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\n",
        "\n",
        "    from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "    pipe = pipeline(\n",
        "        \"text-generation\",\n",
        "        model=model,\n",
        "        tokenizer=tokenizer,\n",
        "        max_new_tokens=CFG2.max_new_tokens,\n",
        "        do_sample=CFG2.do_sample,\n",
        "        temperature=CFG2.temperature,\n",
        "        top_p=CFG2.top_p,\n",
        "        top_k=CFG2.top_k,\n",
        "        repetition_penalty=CFG2.repetition_penalty\n",
        "    )\n",
        "\n",
        "    llm = HuggingFacePipeline(pipeline=pipe)\n",
        "    langchain.llm_cache = InMemoryCache()\n",
        "\n",
        "# prompts\n",
        "from langchain import PromptTemplate, LLMChain\n",
        "\n",
        "prompt_template = \"\"\"<s>[INST] You are given the context after <<CONTEXT>> and a question after <<QUESTION>>.\n",
        "\n",
        "Answer the question by only using the information in context. Only base your answer on the information in the context. Even if you know something more,\n",
        "keep silent about it. It is important that you only tell what can be infered from the context alone.\n",
        "\n",
        "<<QUESTION>>{question}\\n<<CONTEXT>>{context} [/INST]\"\"\"\n",
        "\n",
        "PROMPT = PromptTemplate(\n",
        "    template = prompt_template,\n",
        "    input_variables = [\"question\", \"context\"]\n",
        ")\n",
        "\n",
        "llm_chain = LLMChain(prompt=PROMPT, llm=llm)\n",
        "\n",
        "# we get the context part by embedding retrieval\n",
        "# retrievers\n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.llms import HuggingFaceHub\n",
        "\n",
        "retriever = vectordb.as_retriever(search_kwargs = {\"k\": CFG2.k, \"search_type\" : \"similarity\"})\n",
        "\n",
        "qa_chain = RetrievalQA.from_chain_type(\n",
        "    llm = llm,\n",
        "    chain_type = \"stuff\", # map_reduce, map_rerank, stuff, refine\n",
        "    retriever = retriever,\n",
        "    chain_type_kwargs = {\"prompt\": PROMPT},\n",
        "    return_source_documents = True,\n",
        "    verbose = False\n",
        ")\n",
        "\n",
        "def wrap_text_preserve_newlines(text, width=700):\n",
        "    # Split the input text into lines based on newline characters\n",
        "    lines = text.split('\\n')\n",
        "\n",
        "    # Wrap each line individually\n",
        "    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]\n",
        "\n",
        "    # Join the wrapped lines back together using newline characters\n",
        "    wrapped_text = '\\n'.join(wrapped_lines)\n",
        "\n",
        "    return wrapped_text\n",
        "\n",
        "def process_llm_response(llm_response):\n",
        "    ans = wrap_text_preserve_newlines(llm_response['result'])\n",
        "\n",
        "    sources_used = ' \\n'.join(\n",
        "        [\n",
        "            source.metadata['source'].split('/')[-1][:-4] + ' - page: ' + str(source.metadata['page'])\n",
        "            for source in llm_response['source_documents']\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    ans = ans + '\\n\\nSources: \\n' + sources_used\n",
        "    return ans\n"
      ],
      "metadata": {
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`AnnotionFormat` is deprecated and will be removed in v4.38. Please use `transformers.image_utils.AnnotationFormat` instead.\n"
          ]
        },
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    },
    {
      "cell_type": "code",
      "source": [
        "# @title 4. 💬 ¡Run the cell and after write your question!\n",
        "\n",
        "# For the classical form for to make question, execute the next funtion:\n",
        "# query = \"Ask question?\"\n",
        "#def llm_ans(query):\n",
        "#    llm_response = qa_chain(query)\n",
        "#    ans = process_llm_response(llm_response)\n",
        "#    return ans.strip()\n",
        "\n",
        "def llm_ans():\n",
        "\n",
        "    i = 1\n",
        "    Continue = True\n",
        "    while Continue:\n",
        "      print('\\n Question:', i, '...(Write EXIT for end)...')\n",
        "      print('-----------------')\n",
        "      query = str(input())\n",
        "\n",
        "      Continue = query!= 'EXIT'\n",
        "\n",
        "      if Continue:\n",
        "        llm_response = qa_chain(query)\n",
        "        ans = process_llm_response(llm_response)\n",
        "        print('\\n Answer:', i)\n",
        "        print('-----------------')\n",
        "        print(ans.strip())\n",
        "        i = i + 1\n",
        "\n",
        "llm_ans()"
      ],
      "metadata": {
        "id": "85LbLx29nxly",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "outputId": "41c0a879-62ab-4d6d-9bf9-65b61e91381f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            " Question: 1 ...(Write EXIT for end)...\n",
            "-----------------\n",
            "What is this book about?\n",
            "\n",
            " Answer: 1\n",
            "-----------------\n",
            "This book is about natural language processing (NLP) and how to fine-tune existing models like BERT, GPT2, and T5 for various NLP applications. The book also covers how transformers are being used in areas like vision. All source code used in the book can be found at github.com/apress/intro-transformers-nlp.\n",
            "\n",
            "Sources: \n",
            "hf - page: 1 \n",
            "hf - page: 7 \n",
            "hf - page: 1 \n",
            "hf - page: 5 \n",
            "hf - page: 1\n",
            "\n",
            " Question: 2 ...(Write EXIT for end)...\n",
            "-----------------\n",
            "EXIT\n"
          ]
        }
      ]
    }
  ]
}