2. Memory.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m292.8/292.8 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m20.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m289.1/289.1 kB\u001b[0m \u001b[31m20.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.7/113.7 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.1/141.1 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade langchain openai  -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!pip install -q accelerate -U\n",
        "!pip install transformers==4.31.0 -q\n",
        "!pip install -q bitsandbytes==0.40.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xF69-CxveY8L",
        "outputId": "93ad80ab-f953-4330-f2eb-c95f864a6fdd"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.4/297.4 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m24.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m51.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Langchain: Memory**"
      ],
      "metadata": {
        "id": "JSAArR6leen_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Utilizing 1hr course LangChain for LLM Application Development,, I'm adapting similar methodologies for Llama2 from Hugging Face. Given the limited resources available, this endeavor aims to provide valuable insights and practical applications for individuals who wants to use different styles than chat_models module in langchain**"
      ],
      "metadata": {
        "id": "9Z6WcqMOenbE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "\n",
        "model_id = 'meta-llama/Llama-2-13b-chat-hf'\n",
        "\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "# set quantization configuration to load large model with less GPU memory\n",
        "# this requires the `bitsandbytes` library\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=bfloat16\n",
        ")\n",
        "\n",
        "# begin initializing HF items, need auth token for these\n",
        "hf_auth = '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'\n",
        "model_config = transformers.AutoConfig.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=hf_auth\n",
        ")\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= {\"\": 0},\n",
        "    use_auth_token=hf_auth\n",
        ")\n",
        "model.eval()\n",
        "print(f\"Model loaded on {device}\")"
      ],
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        "id": "pXiovLNOecqQ",
        "outputId": "53dedd8b-b8d5-4fa5-8f55-f2320b3f827e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/587 [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:2193: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/33.4k [00:00<?, ?B/s]"
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
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          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "fff208bf6bfc44d2849f75bc17168e0f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded on cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=hf_auth,\n",
        "     padding_side='left'\n",
        ")\n",
        "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "\n",
        "class MyPipeline:\n",
        "    def __init__(self, model, tokenizer):\n",
        "        self.model = model\n",
        "        self.tokenizer = tokenizer\n",
        "        self.task = \"text-generation\"\n",
        "        self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    def __call__(self, input_text):\n",
        "        # If input_text is a single string, convert it to a list with one element\n",
        "        if isinstance(input_text, str):\n",
        "            input_text = [input_text]\n",
        "\n",
        "        # Tokenize the input text\n",
        "        input_ids = self.tokenizer(input_text, return_tensors='pt', truncation=True,padding=True)['input_ids'].to(self.device)\n",
        "\n",
        "        # Generate text\n",
        "        generated_output = self.model.generate(\n",
        "            input_ids,  # Generate a bit more than the input\n",
        "            temperature=0.1,\n",
        "            repetition_penalty=1.1,\n",
        "            pad_token_id=self.tokenizer.pad_token_id,\n",
        "            eos_token_id=self.tokenizer.eos_token_id\n",
        "        )\n",
        "\n",
        "        # Decode the generated output\n",
        "        generated_texts = []\n",
        "        for i in range(len(input_text)):\n",
        "            generated_text = self.tokenizer.decode(generated_output[i], skip_special_tokens=True)\n",
        "            # Remove the portion of generated text that matches the input length\n",
        "            generated_text = generated_text[len(input_text[i]):]\n",
        "            generated_texts.append({'input_text': input_text[i], 'generated_text': generated_text})\n",
        "\n",
        "        return generated_texts\n",
        "\n",
        "\n",
        "\n",
        "text_generator = MyPipeline(model=model, tokenizer=tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180,
          "referenced_widgets": [
            "015d0ba308e0411f99d0a68c8d3aeb43",
            "294c429bddc541b2af95137a463ea775",
            "3b83e409991f40ee8be0cfded1e8a407",
            "7aeb9f5116574f7da4d9e064f917374b",
            "7af3d09f84de4f1e9452d83b95266623",
            "b5cd8ee4e6954a8b8113ca215f2bb5be",
            "2e81993b2b07455896b2d534e94ca3a1",
            "024d797a98384857902c919b20b4ed0d",
            "cfdbf924c70f4291aeb6b258f6b8a33b",
            "ed1bd6200cde4259b560435d9fde67c5",
            "fe8bcedc5f2e49a9b6f42201dcbc29f4",
            "0c2a5383a9be4c83bf3d65f50b7e5e45",
            "8d048e35ef5948829766841f279c0ceb",
            "3c937829eb814d47b8cb8679ce1db0f9",
            "080313a6b72a4f2e8a5770bd6b87915c",
            "2f9d81ed1d7c4e5f8f914779535022db",
            "a3e90113083f48ca82aa7543748388df",
            "deab74a40473487a8a08413e64512b87",
            "515a517c9adb40ea807a5f5e3ccb4217",
            "573fdc86fdf9487abe84f982ade1a4f1",
            "cbfcdc3b0ba84dc0ba0b30d9168f5ead",
            "d70283bde1ed46e4bf383bb3ef1fc786",
            "45f7523779bf4cba9810b7df6a89016e",
            "69f6c5669a3744c7826523979f2a5533",
            "b182bd06230b44d8ab14a30fbfdbb3de",
            "d79218042cf84c5086507bf0ae5e7a78",
            "324e80d53d234d2abf88ac6ce9c3ed1e",
            "dbce66275c0b40f680e527b42707a6c4",
            "53f03976ae00442b8c47d6a1e9ab1650",
            "2f5a2df0cad44ad9910199afad8e9797",
            "47eb8e88f07e437983d2607554fecede",
            "3889526b1dcf4a0a8616971988a6756d",
            "afd8df0fc49b4cbc86e792a7d6048075",
            "61a3d58f1321450ca936b19d1b7d6252",
            "8c36f66ad23e415baf48628322d721d3",
            "19b392d47f3c42899a3fc9b2fb8049d6",
            "98e2c2c27e93472d8b99c49efc34c7fa",
            "b0eb8441b92944cd9ea9252f085ba7b7",
            "3d0a8fbc80774d81a7c394196d66f321",
            "460ed7ea5e46419db7410d6475005538",
            "da9878de24ef44d59135463a0469bdde",
            "3d1db9ce5abe4fc5b7d2e3dceb0d32e9",
            "4b2f62bb9d3f4e368630a9a141b2b7bb",
            "a0cff4a92db247c185ac3baf437bff19"
          ]
        },
        "id": "aZlcz8OCeuzW",
        "outputId": "20cc2205-2da4-4577-daed-efcfbf6d15dd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "015d0ba308e0411f99d0a68c8d3aeb43"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1714: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c2a5383a9be4c83bf3d65f50b7e5e45"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "45f7523779bf4cba9810b7df6a89016e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_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": "61a3d58f1321450ca936b19d1b7d6252"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=text_generator)"
      ],
      "metadata": {
        "id": "R3wtU74eiqZj"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. I beleive these memories can't be used in production as this uses RAM,as the conversation size increases with user base we'll have to store conversations in a database.**"
      ],
      "metadata": {
        "id": "A6aE6x1ojbGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import ConversationChain\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "\n",
        "memory = ConversationBufferMemory()\n",
        "conversation = ConversationChain(\n",
        "    llm=llm,\n",
        "    memory = memory,\n",
        "    verbose=True\n",
        ")"
      ],
      "metadata": {
        "id": "UDoKsyQDiuo6"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conversation.predict(input=\"[INST]Hey, My name is Abhiram. Who are you?[/INST]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "UqB4E7HVjgl1",
        "outputId": "56d95469-495a-4122-a6b6-9ecdd0a40103"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
            "Prompt after formatting:\n",
            "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
            "\n",
            "Current conversation:\n",
            "\n",
            "Human: [INST]Hey, My name is Abhiram. Who are you?[/INST]\n",
            "AI:\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (4096) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\" Hi there! I'm LLaMA, I'm a large language model trained by a team of researcher at Meta AI. How can I help you today? By the way, I noticed that your name is Abhiram, that's a pretty unique name! Do you mind me asking where you heard that name before? I'm just curious because I've never come across anyone with that name before. Oh, and by the way, do you like pizza? I love pizza! What's your favorite topping?\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conversation.predict(input=\"[INST]What stable diffusion? Explain in one sentence[/INST]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "DjPm5_YwjvBL",
        "outputId": "c6c208a8-e183-4b5a-a37b-3ded54e137ef"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
            "Prompt after formatting:\n",
            "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
            "\n",
            "Current conversation:\n",
            "Human: [INST]Hey, My name is Abhiram. Who are you?[/INST]\n",
            "AI:  Hi there! I'm LLaMA, I'm a large language model trained by a team of researcher at Meta AI. How can I help you today? By the way, I noticed that your name is Abhiram, that's a pretty unique name! Do you mind me asking where you heard that name before? I'm just curious because I've never come across anyone with that name before. Oh, and by the way, do you like pizza? I love pizza! What's your favorite topping?\n",
            "Human: [INST]What stable diffusion? Explain in one sentence[/INST]\n",
            "AI:\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (4096) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'  Sure thing! Stable diffusion is a type of stochastic process that exhibits stable distribution properties, meaning that the distribution of the process remains unchanged when scaled or transformed. In other words, the shape of the distribution remains the same regardless of the magnitude of the values. Would you like me to explain further or provide an example?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conversation.predict(input=\"[INST]What is my name?[/INST]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "G_xnZvQwkKMc",
        "outputId": "f5bd34cc-4f09-48f9-ab61-7fcb5423b2e5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
            "Prompt after formatting:\n",
            "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
            "\n",
            "Current conversation:\n",
            "Human: [INST]Hey, My name is Abhiram. Who are you?[/INST]\n",
            "AI:  Hi there! I'm LLaMA, I'm a large language model trained by a team of researcher at Meta AI. How can I help you today? By the way, I noticed that your name is Abhiram, that's a pretty unique name! Do you mind me asking where you heard that name before? I'm just curious because I've never come across anyone with that name before. Oh, and by the way, do you like pizza? I love pizza! What's your favorite topping?\n",
            "Human: [INST]What stable diffusion? Explain in one sentence[/INST]\n",
            "AI:   Sure thing! Stable diffusion is a type of stochastic process that exhibits stable distribution properties, meaning that the distribution of the process remains unchanged when scaled or transformed. In other words, the shape of the distribution remains the same regardless of the magnitude of the values. Would you like me to explain further or provide an example?\n",
            "Human: [INST]What is my name?[/INST]\n",
            "AI:\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (4096) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'  Your name is Abhiram.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(memory.buffer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9FPayD9hke1T",
        "outputId": "77eb56b2-11e9-43ea-aa91-509c9d2b6506"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Human: [INST]Hey, My name is Abhiram. Who are you?[/INST]\n",
            "AI:  Hi there! I'm LLaMA, I'm a large language model trained by a team of researcher at Meta AI. How can I help you today? By the way, I noticed that your name is Abhiram, that's a pretty unique name! Do you mind me asking where you heard that name before? I'm just curious because I've never come across anyone with that name before. Oh, and by the way, do you like pizza? I love pizza! What's your favorite topping?\n",
            "Human: [INST]What stable diffusion? Explain in one sentence[/INST]\n",
            "AI:   Sure thing! Stable diffusion is a type of stochastic process that exhibits stable distribution properties, meaning that the distribution of the process remains unchanged when scaled or transformed. In other words, the shape of the distribution remains the same regardless of the magnitude of the values. Would you like me to explain further or provide an example?\n",
            "Human: [INST]What is my name?[/INST]\n",
            "AI:   Your name is Abhiram.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "memory.load_memory_variables({})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYYeheVfkg2K",
        "outputId": "41aead50-63f1-47aa-f88d-f74bb0a70c04"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'history': \"Human: [INST]Hey, My name is Abhiram. Who are you?[/INST]\\nAI:  Hi there! I'm LLaMA, I'm a large language model trained by a team of researcher at Meta AI. How can I help you today? By the way, I noticed that your name is Abhiram, that's a pretty unique name! Do you mind me asking where you heard that name before? I'm just curious because I've never come across anyone with that name before. Oh, and by the way, do you like pizza? I love pizza! What's your favorite topping?\\nHuman: [INST]What stable diffusion? Explain in one sentence[/INST]\\nAI:   Sure thing! Stable diffusion is a type of stochastic process that exhibits stable distribution properties, meaning that the distribution of the process remains unchanged when scaled or transformed. In other words, the shape of the distribution remains the same regardless of the magnitude of the values. Would you like me to explain further or provide an example?\\nHuman: [INST]What is my name?[/INST]\\nAI:   Your name is Abhiram.\"}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conversation Buffer Memory**"
      ],
      "metadata": {
        "id": "0i8-sKu7kkb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "memory = ConversationBufferMemory()"
      ],
      "metadata": {
        "id": "w-hw1kQNkm6M"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "memory.save_context({\"input\": \"Hi\"},\n",
        "                    {\"output\": \"What's up\"})"
      ],
      "metadata": {
        "id": "WeHYvPQgkp1q"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(memory.buffer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srs5H5wWks0w",
        "outputId": "f324883d-7aa2-434b-a190-4249067a1848"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Human: Hi\n",
            "AI: What's up\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "memory.load_memory_variables({})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CIbiaIUnkvMf",
        "outputId": "2a09b873-fcef-4a3e-df50-b077b74062b2"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'history': \"Human: Hi\\nAI: What's up\"}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "memory.save_context({\"input\": \"Not much, just hanging\"},\n",
        "                    {\"output\": \"Cool\"})"
      ],
      "metadata": {
        "id": "SbwBPpl1kxt4"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "memory.load_memory_variables({})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qRse3D6ilNp2",
        "outputId": "8555a76c-a904-434a-8251-658fe43aed6b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'history': \"Human: Hi\\nAI: What's up\\nHuman: Not much, just hanging\\nAI: Cool\"}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.memory import ConversationBufferWindowMemory\n",
        "\n",
        "memory = ConversationBufferWindowMemory(k=1)\n",
        "\n",
        "memory.save_context({\"input\": \"Hi\"},\n",
        "                    {\"output\": \"What's up\"})\n",
        "memory.save_context({\"input\": \"Not much, just hanging\"},\n",
        "                    {\"output\": \"Cool\"})\n",
        "\n",
        "memory.load_memory_variables({})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZvEoTgtlQOW",
        "outputId": "72e6d309-67e3-425f-95a3-a0e641491cda"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'history': 'Human: Not much, just hanging\\nAI: Cool'}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conversation Buffer Window Memory**"
      ],
      "metadata": {
        "id": "J1J-Iyu1lV4q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "memory = ConversationBufferWindowMemory(k=1)\n",
        "conversation = ConversationChain(\n",
        "    llm=llm,\n",
        "    memory = memory,\n",
        "    verbose=False\n",
        ")"
      ],
      "metadata": {
        "id": "fza0UHXFlTdk"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conversation.predict(input=\"[INST]Hi, my name is ABHIRAM[/INST]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "qSuWqCdzlaHg",
        "outputId": "7ba84702-b24d-4c84-ab9d-5b3e69c238d4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (4096) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\" Hi there! My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI. It's nice to meet you, ABHIRAM! How can I assist you today? Would you like to chat about a particular topic or ask me some questions? I have a vast knowledge base and can provide information on a wide range of subjects.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conversation.predict(input=\"[/INST]What is 10+1?[/INST]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "xDeJl3lZlhe3",
        "outputId": "dece80fe-b003-4abc-e89c-c116adcb36da"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (4096) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"  Oh, that's an easy one! 10 + 1 is equal to 11. Do you have any other questions for me? I'm here to help with any inquiries you may have. By the way, do you know what the weather is like today in your area? I can provide you with the current conditions and forecast if you'd like.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conversation.predict(input=\"[INST]What is my name?[/INST]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "DcvXq_fHlosx",
        "outputId": "c35d48a3-9614-42b4-bde3-a7728f03e5f3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (4096) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"  Hmm... I don't know your name. I'm just an AI, I don't have access to personal information or the ability to recognize individuals. However, if you'd like, I can suggest some possible names based on popular culture or random selection. Or perhaps you'd prefer a more unique or creative name? Let me know and I'll do my best to assist you!\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**There are other memories too. ConversationTokenBufferMemory, ConversationSummaryMemory. I am skipping them in this notebook**"
      ],
      "metadata": {
        "id": "GeafKjPxl0IR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KKv7QChMlwKb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}