03a-token-counter.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#### [LangChain Handbook](https://pinecone.io/learn/langchain)\n",
        "\n",
        "# Conversational Memory\n",
        "\n",
        "## Extra Material: Token Counter\n",
        "\n",
        "This is an additional piece of material alongside the [LangChain Handbook notebook on Conversational Memory](https://github.com/pinecone-io/examples/blob/master/generation/langchain/handbook/03-langchain-conversational-memory.ipynb).\n",
        "\n",
        "In this notebook we will count the number of tokens used in a conversation for different conversational memory types.\n",
        "\n",
        "We begin by installing the required libraries:"
      ],
      "metadata": {
        "id": "ytU8AV9uMty3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iK0npZ4Mgae",
        "outputId": "f79e4b83-b1f2-4610-b032-6fbb042c3872"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qU langchain openai transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import required libraries and objects:"
      ],
      "metadata": {
        "id": "g6-xDqLGRYsh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from getpass import getpass\n",
        "\n",
        "import openai\n",
        "from langchain import OpenAI\n",
        "from langchain.chains import LLMChain, ConversationChain\n",
        "from langchain.chains.conversation.memory import (\n",
        "    ConversationBufferMemory,\n",
        "    ConversationSummaryMemory,\n",
        "    ConversationBufferWindowMemory,\n",
        "    ConversationSummaryBufferMemory\n",
        ")\n",
        "from langchain.callbacks import get_openai_callback\n",
        "from tqdm.auto import tqdm"
      ],
      "metadata": {
        "id": "4nzzNSCxRcHS"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To run the notebook we'll use OpenAI's `gpt-3.5-turbo` model. We initialize it via LangChain like so:"
      ],
      "metadata": {
        "id": "2zWvthQzUUzC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "OPENAI_API_KEY = getpass()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvimTSA4Ublb",
        "outputId": "075282fa-b447-4e87-9d31-bf5d8966fc08"
      },
      "execution_count": 3,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm = OpenAI(\n",
        "    temperature=0, \n",
        "    openai_api_key=OPENAI_API_KEY,\n",
        "    model_name='gpt-3.5-turbo'  # can be used with llms like 'text-davinci-003'\n",
        ")"
      ],
      "metadata": {
        "id": "oRjdCaSaUdCd"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To count the number of tokens used during each call we will define a `count_tokens` function:"
      ],
      "metadata": {
        "id": "fUv5H5SWUgNt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def count_tokens(chain, query):\n",
        "    with get_openai_callback() as cb:\n",
        "        result = chain.run(query)\n",
        "    return {\n",
        "        'result': result,\n",
        "        'token_count': cb.total_tokens\n",
        "    }"
      ],
      "metadata": {
        "id": "EqTuGsENUnAr"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's define the conversation function:"
      ],
      "metadata": {
        "id": "OvCBfg9gUst-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "queries = [\n",
        "    \"Good morning AI?\",\n",
        "    \"\"\"My interest here is to explore the potential of integrating Large\n",
        "    Language Models with external knowledge\"\"\",\n",
        "    \"I just want to analyze the different possibilities. What can you think of?\",\n",
        "    \"What about the use of retrieval augmentation, can that be used as well?\",\n",
        "    \"\"\"That's very interesting, can you tell me more about this? Like what\n",
        "    systems would I use to store the information and retrieve relevant info?\"\"\",\n",
        "    \"\"\"Okay that's cool, I've been hearing about 'vector databases', are they\n",
        "    relevant in this context?\"\"\",\n",
        "    \"\"\"Okay that's useful, but how do I go from my external knowledge to\n",
        "    creating these 'vectors'? I have no idea how text can become a vector?\"\"\",\n",
        "    \"\"\"Well I don't think I'd be using word embeddings right? If I wanted to\n",
        "    store my documents in this vector database, I suppose I would need to\n",
        "    transform the documents into vectors? Maybe I can use the 'sentence\n",
        "    embeddings' for this, what do you think?\"\"\",\n",
        "    \"\"\"Can sentence embeddings only represent sentences of text? That seems\n",
        "    kind of small to capture any meaning from a document? Is there any approach\n",
        "    that can encode at least a paragraph of text?\"\"\",\n",
        "    \"\"\"Huh, interesting. I do remember reading something about 'mpnet' or\n",
        "    'minilm' sentence 'transformer' models that could encode small to\n",
        "    medium sized paragraphs. Am I wrong about this?\"\"\",\n",
        "    \"\"\"Ah that's great to hear, do you happen to know how much text I can feed\n",
        "    into these types of models?\"\"\",\n",
        "    \"\"\"I've never heard of hierarchical embeddings, could you explain those in\n",
        "    more detail?\"\"\",\n",
        "    \"\"\"So is it like you have a transformer model or something else that creates\n",
        "    sentence level embeddings, then you feed all of the sentence level\n",
        "    embeddings into another separate neural network that knows how to merge\n",
        "    multiple sentence embeddings into a single embedding?\"\"\",\n",
        "    \"\"\"Could you explain this process step by step from start to finish? Explain\n",
        "    like I'm very new to this space, assume I don't have much prior knowledge\n",
        "    of embeddings, neural nets, etc\"\"\",\n",
        "    \"\"\"Awesome thanks! Are there any popular 'heirarchical neural network'\n",
        "    models that I can look up? Or maybe just the second stage that creates the\n",
        "    hierarchical embeddings?\"\"\",\n",
        "    \"It seems like these HAN models are quite old, is there anything more recent?\",\n",
        "    \"Can you explain the difference between transformer-XL and longformer?\",\n",
        "    \"How much text can be encoded by each of these models?\",\n",
        "    \"\"\"Okay very interesting, so before returning to earlier in the conversation.\n",
        "    I understand now that there are a lot of different transformer (and not\n",
        "    transformer) based models for creating the embeddings from vectors. Is that\n",
        "    correct?\"\"\",\n",
        "    \"\"\"Perfect, so I understand text can be encoded into these embeddings. But\n",
        "    what then? Once I have my embeddings what do I do?\"\"\",\n",
        "    \"\"\"I'd like to use these embeddings to help a chatbot or a question-answering\n",
        "    system answer questions with help from this external knowledge base. I\n",
        "    suppose this would come under information retrieval? Could you explain that\n",
        "    process in a little more detail?\"\"\",\n",
        "    \"\"\"Okay great, that sounds like what I'm hoping to do. When you say the\n",
        "    'chatbot or question-answering system generates an embedding', what do you\n",
        "    mean exactly?\"\"\",\n",
        "    \"\"\"Ah okay, I understand, so it isn't the 'chatbot' model specifically\n",
        "    creating the embedding right? That's how I understood your earlier comment.\n",
        "    It seems more like there is a separate embedding model? And that encodes\n",
        "    the query, then we retrieve the set of relevant documents from the\n",
        "    external knowledge base? How is that information then used by the chatbot\n",
        "    or question-answering system exactly?\"\"\",\n",
        "    \"\"\"Okay but how is the information provided to the chatbot or\n",
        "    question-answering system?\"\"\",\n",
        "    \"\"\"So the retrieved information is given to the chatbot / QA system as plain\n",
        "    text? But then how do we pass in the original query? How can the system\n",
        "    distinguish between a user's query and all of this additional information?\"\"\",\n",
        "    \"\"\"That doesn't seem correct to me, my question is — if we are giving the\n",
        "    chatbot / QA system the user's query AND retrieved information from an\n",
        "    external knowledge base, and it's all fed into the model as plain text,\n",
        "    how does the model know what part of the plain text is a query vs. retrieved\n",
        "    information?\"\"\",\n",
        "    \"\"\"Yes I get that, but in the text passed to the model, how do we identify\n",
        "    user prompt vs retrieved information?\"\"\"\n",
        "\n",
        "]\n",
        "\n",
        "def talk(conversation_chain):\n",
        "    tokens_used = []\n",
        "    # we loop through the conversation above, counting token usage as we go\n",
        "    for user_query in tqdm(queries):\n",
        "        try:\n",
        "            res = count_tokens(conversation_chain, user_query)\n",
        "            tokens_used.append(res['token_count'])\n",
        "        except openai.error.InvalidRequestError:\n",
        "            # we hit the token limit of the model, so break\n",
        "            break\n",
        "    return tokens_used"
      ],
      "metadata": {
        "id": "tEjQ7YZwU_rM"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create set of conversation chains that we'll be using:"
      ],
      "metadata": {
        "id": "Xprpc4qGC0aS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conversation_chains = {\n",
        "    'ConversationBufferMemory': ConversationChain(\n",
        "        llm=llm, memory=ConversationBufferMemory()\n",
        "    ),\n",
        "    'ConversationSummaryMemory': ConversationChain(\n",
        "        llm=llm, memory=ConversationSummaryMemory(llm=llm)\n",
        "    ),\n",
        "    'ConversationBufferWindowMemory(k=6)': ConversationChain(\n",
        "        llm=llm, memory=ConversationBufferWindowMemory(k=6)\n",
        "    ),\n",
        "    'ConversationBufferWindowMemory(k=12)': ConversationChain(\n",
        "        llm=llm, memory=ConversationBufferWindowMemory(k=12)\n",
        "    ),\n",
        "    'ConversationSummaryBufferMemory(k=6)': ConversationChain(\n",
        "        llm=llm, memory=ConversationSummaryBufferMemory(\n",
        "            llm=llm,\n",
        "            max_token_limit=650\n",
        "        )\n",
        "    ),\n",
        "    'ConversationSummaryBufferMemory(k=12)': ConversationChain(\n",
        "        llm=llm, memory=ConversationSummaryBufferMemory(\n",
        "            llm=llm,\n",
        "            max_token_limit=1_300\n",
        "        )\n",
        "    )\n",
        "}"
      ],
      "metadata": {
        "id": "I53dXttjDCgA"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counts = {}\n",
        "# loop through each of our memory types above\n",
        "for key, chain in conversation_chains.items():\n",
        "    print(key)\n",
        "    counts[key] = talk(chain)"
      ],
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            "c49af1d9b28741299e25dd2646099e23"
          ]
        },
        "id": "Rxirb9Vt5oOj",
        "outputId": "1a29133b-e7f2-4ac3-a80a-2dd674e3d8c7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConversationBufferMemory\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2be4d4a7e596437db88f541b8d34bc53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConversationSummaryMemory\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b177914703a847479f2e5b876dbdf4f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConversationBufferWindowMemory(k=6)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2cd828e3928f45cc8cf6e0050cd4f6a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConversationBufferWindowMemory(k=12)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86387f7978d24544bc10cae2c488928a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConversationSummaryBufferMemory(k=6)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4455e527a2744efc88c42f1cbec1536e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0cb7de9161b2409aa38b1860eb8cd990"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79826df9c5cc404f95d4ce8ebcfa20ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9dc25233e04e4b8cb3a6fa79f7eb1e23"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ff386b5a15b43f4b75ba4c6fa6959a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConversationSummaryBufferMemory(k=12)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6193595b5824404b8e6c9428612d67ee"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "max_tokens = 4096\n",
        "\n",
        "colors = [\"#1c17ff\", \"#738FAB\", \"#f77f00\", \"#fcbf49\", \"#38c172\", \"#4dc0b5\"]\n",
        "\n",
        "for i, (key, count) in enumerate(counts.items()):\n",
        "    color = colors[i]\n",
        "    sns.lineplot(\n",
        "        x=range(1, len(count)+1),\n",
        "        y=count,\n",
        "        label=key,\n",
        "        color=color\n",
        "    )\n",
        "    if max_tokens in count:\n",
        "        plt.plot(\n",
        "            len(count), max_tokens, marker=\"X\", color=\"red\", markersize=10\n",
        "        )\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "HMRC22_z72kr",
        "outputId": "74ba8538-bc50-4bca-d766-9768b1d449e7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "jZuSLqremRX0"
      }
    }
  ]
}