Llama_7B.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRYSu48huSUW",
        "outputId": "b9905317-7af5-4543-98e0-ad329bd093ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hCollecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.33.2)\n",
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            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.13.3)\n",
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            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=0c721292a32548af4ffa272000c6bf4d97a3fae57833e7cf2d2e7cc42fbd1f45\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2\n"
          ]
        }
      ],
      "source": [
        "!pip -q install langchain tiktoken chromadb pypdf transformers InstructorEmbedding\n",
        "!pip -q install accelerate bitsandbytes sentencepiece Xformers\n",
        "!pip install sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPbWcfYeCGEJ",
        "outputId": "556a8856-99a1-4189-c170-bb1f9b80caec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
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            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGi6vIHBJE19",
        "outputId": "a2dfecf7-9a47-43e5-bfa7-d6bf4b67909e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (0.56.4)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba) (0.39.1)\n",
            "Requirement already satisfied: numpy<1.24,>=1.18 in /usr/local/lib/python3.10/dist-packages (from numba) (1.23.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba) (67.7.2)\n",
            "<CUDA device 0 'b'Tesla T4''>\n"
          ]
        }
      ],
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "!pip install numba\n",
        "\n",
        "from numba import cuda\n",
        "device = cuda.get_current_device()\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAdw3ADcS8Ph"
      },
      "source": [
        "## Generate LLama2 Model\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509,
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            "d2724851ddda42ff8e8c396e082ad308",
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            "cf30f5b5e1404672b936b0e535e7e045",
            "5fac2ae11cee43be8e6e62ea2a5dcecc",
            "489e4cd11de84a7897a6bd7ab8c7c7ff",
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            "65445f4099f44e4e89399ab0c0badb99",
            "eb1d2455058f4c0fa7665b03e40bd77b",
            "5f2bf2af19cc43589e6023e8d0a5096e",
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            "6a4b13cc5ec84ace9550f4eee4ef1096",
            "90fd57789e094ae7a2ecff25b39ef08b",
            "a7c17dc5037b45b0bc3c54f575b83756",
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            "237a71aec9064a3d995924c652bf9dc2",
            "090d6c50d81b4adaa2fd9057528fe634",
            "820a1c42ca2641859634e36200a65b5c",
            "a06cefa5231f46e99b8029bfd78b543f",
            "463df15d513449609cf7ba4a20fafe64",
            "5244f3808eb14883b821def73497c26c",
            "98d8a6efb8c74b7685712e8f0c865c42",
            "a63be34d6e8c4af785e554dcafe6662f",
            "f2165ef1435443f6b5bfb2d8144677d1",
            "605cc6e76ba64826a300a6d6a276b428",
            "6d5baaafe38349d6ad6b69c24daf4086",
            "872eb93ea647444e9f5340929cc02384",
            "ea027b7a740c4541a41835ac69807877",
            "76a32d605a2c4af7ac10d46252cbd297",
            "fe779f223ef94cf3aedf96b154204889",
            "b2e50e0b1b1747059a4707e779b7a258",
            "9c6f4db02f0948e987d59d3a5a074186",
            "8e055024f22f4900b6a171c66db0d66f",
            "d9a055dea00f4818bd182719043bcd1b"
          ]
        },
        "id": "_38HzUkvIewb",
        "outputId": "b9cd3df1-3c35-45fc-df5f-b462f1f4b258"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/776 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f7a0405bb7eb49c388d8cc832c2ea9bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b96718b070e41d4ace603c49140c3ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9db713604db54f5c81e588815d560b41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf30f5b5e1404672b936b0e535e7e045"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e99c170c91df4efcb34c8f485c07ee51"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)fetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c2f9bb01b594742ba02e0d530bc5bfd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ec258716cbe40e5bdc0539dc9966a23"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6969842433b94eae89a294ca77afe1ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a7f8cff4cd34825a3a75e185f020fac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d08748147f414ce2a90f6caa37d2baf6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2165ef1435443f6b5bfb2d8144677d1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Llama-2-7b-chat-hf\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "                                             load_in_8bit=True,\n",
        "                                              device_map='auto',\n",
        "                                              torch_dtype=torch.float16,\n",
        "                                              low_cpu_mem_usage=True)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create The Pipeline from the loaded Llama2 Model and tokenizer"
      ],
      "metadata": {
        "id": "dWTvkxW7CCx5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "6G98f4fXI5kT"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=2048,\n",
        "    temperature=0.01,\n",
        "    top_p=0.95,\n",
        "    repetition_penalty=1.15\n",
        ")\n",
        "\n",
        "local_llm = HuggingFacePipeline(pipeline=pipe)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ask something"
      ],
      "metadata": {
        "id": "zeMypIIVCOSI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vZ6FB4R8K42I",
        "outputId": "b1b85b7b-aa5d-406b-ddd3-8f1ba5652247"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Llama2 is a cryptocurrency that was created in 2019 as an alternative to the popular privacy-focused coin, Monero (XMR). While both coins share some similarities, there are several key differences between them. Here are some of the main differences:\n",
            "1. Mining Algorithm: Llama2 uses the CryptoNightV7 mining algorithm, which is different from Monero's Random Xorshift++ algorithm. This means that Llama2 has a different proof-of-work mechanism and may be more or less difficult to mine depending on the hardware used.\n",
            "2. Block Time: The block time for Llama2 is shorter than that of Monero, at around 60 seconds compared to Monero's 2 minutes. This faster block time allows for more frequent transactions and potentially higher throughput.\n",
            "3. Transaction Capacity: Llama2 has a larger transaction capacity than Monero, with a maximum of 50,000 transactions per block compared to Monero's 144 transactions per block. This could make Llama2 more suitable for high-volume transactions.\n",
            "4. Privacy Features: Both coins have privacy features built into their design, but Llama2 goes further by implementing zero-knowledge proofs and other advanced privacy technologies. However, it's worth noting that these features are still developing and may not yet be fully mature.\n",
            "5. Community Support: Monero has a much larger and more established community than Llama2, with a longer history and more resources available. This could give Monero an advantage in terms of development speed and overall adoption.\n",
            "Ultimately, whether Llama2 is \"better\" than Monero depends on your specific needs and priorities. If you value fast transaction times and high transaction capacity, Llama2 may be the better choice. But if you prioritize privacy and security, Monero may still be the safer bet due to its longer history and more established reputation.\n"
          ]
        }
      ],
      "source": [
        "print(local_llm('What do you know about Llama2? Answer short in one sentence.'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UcQKUId3X2M"
      },
      "source": [
        "## Load multiple and process pdfs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "PRSeXXc_3Ypj"
      },
      "outputs": [],
      "source": [
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain.document_loaders import DirectoryLoader\n",
        "\n",
        "loader = DirectoryLoader('./input', glob=\"./*.pdf\", loader_cls=PyPDFLoader)\n",
        "\n",
        "documents = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vT6KgAIT_BtB",
        "outputId": "cc5d2893-401b-4cec-d120-b00b74d054ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "len(documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "3__nT0D4Fkmg"
      },
      "outputs": [],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "#splitting the text into\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
        "texts = text_splitter.split_documents(documents)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhs0C0FYASlM"
      },
      "source": [
        "## HF Instructor Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633,
          "referenced_widgets": [
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            "5ea4396a594640e5ad57df1ce769cfc7",
            "3520cfaaa3044f189ebffdccea34b79c",
            "656070487a684c6ebec2da88c6ac4eed",
            "3eef2d49a63d4799a1cc7493c848615b",
            "acad545fb6344c9bbd2e69b5830a83cc",
            "fa49088ac21f4ea59c10301c7c878a49",
            "dc9be75a61de44f0952920d3416a9f4a",
            "6af020b80d814142a87af4860a3f211f",
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          "text": [
            "load INSTRUCTOR_Transformer\n",
            "max_seq_length  512\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
        "\n",
        "instructor_embeddings = HuggingFaceInstructEmbeddings(model_name=\"BAAI/bge-large-en-v1.5\",\n",
        "                                                      model_kwargs={\"device\": \"cuda\"})\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YsYsIy8F4cdm"
      },
      "source": [
        "## create the DB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Q_eTIZwf4Dk2"
      },
      "outputs": [],
      "source": [
        "from langchain.vectorstores import Chroma\n",
        "\n",
        "persist_directory = 'db'\n",
        "\n",
        "vectordb = Chroma.from_documents(documents=texts,\n",
        "                                 embedding=instructor_embeddings,\n",
        "                                 persist_directory=persist_directory)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siLXR-XT0JoI"
      },
      "source": [
        "## Make a retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "jVWgPJXs1yRq"
      },
      "outputs": [],
      "source": [
        "retriever = vectordb.as_retriever(search_kwargs={\"k\": 3})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ia-4OXa5IeP"
      },
      "source": [
        "## Make a chain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "MGx8XblM4shW"
      },
      "outputs": [],
      "source": [
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "# create the chain to answer questions\n",
        "system_template = \"\"\"You are an LLM specialist. Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
        "\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "Helpful Answer:\"\"\"\n",
        "\n",
        "        # Create the chat prompt templates\n",
        "PROMPT = PromptTemplate(template=system_template, input_variables=['context', 'question'])\n",
        "\n",
        "qa_chain = RetrievalQA.from_chain_type(llm=local_llm,\n",
        "                                  chain_type=\"stuff\",\n",
        "                                  retriever=retriever,\n",
        "                                  chain_type_kwargs={\"prompt\": PROMPT},\n",
        "                                  return_source_documents=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "LZEo26mw8e5k"
      },
      "outputs": [],
      "source": [
        "## Cite sources\n",
        "\n",
        "import textwrap\n",
        "\n",
        "def wrap_text_preserve_newlines(text, width=110):\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",
        "    wrapped_text = '\\n'.join(wrapped_lines)\n",
        "\n",
        "    return wrapped_text\n",
        "\n",
        "def process_llm_response(llm_response):\n",
        "    print(wrap_text_preserve_newlines(llm_response['result']))\n",
        "    print('\\n\\nSources:')\n",
        "    for source in llm_response[\"source_documents\"]:\n",
        "        print(source.metadata['source'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "wKfX4vX-5RFT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0891fa0a-9607-45ff-9998-5768f22897f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Llama2 is a new open-source large language model developed by Meta AI that is still under development and\n",
            "designed to be a versatile and powerful LLM for various tasks, including query resolution, natural language\n",
            "comprehension, and reading comprehension.\n",
            "\n",
            "\n",
            "Sources:\n",
            "input/llms.pdf\n",
            "input/embeddings.pdf\n",
            "input/embeddings.pdf\n"
          ]
        }
      ],
      "source": [
        "# full example\n",
        "query = \"What do you know about Llama2? Answer short in one sentence.\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPIhZWAR5n3X",
        "outputId": "079ccd95-5f20-4f7c-bbb0-e741fe7d8e64"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('similarity', <langchain.vectorstores.chroma.Chroma at 0x7d4060235d20>)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "qa_chain.retriever.search_type , qa_chain.retriever.vectorstore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3_lp0_796P_-"
      },
      "outputs": [],
      "source": [
        "print(qa_chain.combine_documents_chain.llm_chain.prompt.template)"
      ]
    }
  ],
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