{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjvV7jJ45z4I"
      },
      "source": [
        "Source: https://www.pinecone.io/learn/openai-gen-qa/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "449.09s - pydevd: Sending message related to process being replaced timed-out after 5 seconds\n",
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install -qU openai pinecone datasets cohere tiktoken --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K1aROn2v57nB",
        "outputId": "31557e01-7ad3-4a86-b2c1-8c5c9dd6fe6f"
      },
      "outputs": [],
      "source": [
        "# Get the openai secret key\n",
        "import getpass\n",
        "\n",
        "OPENAI_API_KEY = getpass.getpass('Please enter your openai key: ')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "OiA3F9lU52jw"
      },
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "# Get API key from top-right dropdown on OpenAI website\n",
        "client = OpenAI(api_key=OPENAI_API_KEY)\n",
        "MODEL = \"gpt-4.1-mini\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "iYQnXTcV6MBY",
        "outputId": "3dd8943d-ab01-4027-bae8-2054abe6152f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'The 12th person to walk on the Moon was Eugene Cernan. He landed on the Moon during the Apollo 17 mission, which touched down on December 11, 1972.'"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "query = \"who was the 12th person on the moon and when did they land?\"\n",
        "\n",
        "# Now query WITHOUT context\n",
        "res = client.chat.completions.create(\n",
        "    model=MODEL,\n",
        "    messages=[\n",
        "        {\n",
        "            \"role\": \"user\",\n",
        "            \"content\": query,\n",
        "        }\n",
        "    ]\n",
        ")\n",
        "\n",
        "res.choices[0].message.content\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "HJ2ibIyX6PHR",
        "outputId": "e1c99c7b-1467-47b6-91aa-99fd0614fdd7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'If you only have pairs of related sentences (i.e., positive pairs) and no explicit negative pairs or labels, a common and effective training method for sentence transformers is **training with a contrastive or triplet loss using semi-supervised or hard-negative mining strategies**, or leveraging **unsupervised/similarity-based losses**.\\n\\nHere are some common approaches you can take:\\n\\n### 1. **If you have only positive pairs:**\\n\\n- **Use a Contrastive Loss with Random Negatives:**  \\n  You can treat each sentence pair as a positive pair and sample random sentences from your batch as negative pairs (in-batch negatives). This is a widely used approach in sentence embedding training.  \\n  - Example: **InfoNCE / Multiple negatives ranking loss / NT-Xent loss**  \\n  - Implementation: For each anchor-positive pair in the batch, all other sentences in the batch act as negatives. This encourages the model to pull related pairs close and push unrelated ones apart.\\n\\n- **Use Multiple Negatives Ranking Loss:**  \\n  Sentence Transformers library provides a `MultipleNegativesRankingLoss` which assumes that other sentences in the batch are negatives of the current pair. So you only need positive pairs.\\n\\n- **Augment your dataset with data augmentation:**  \\n  If possible, create additional positive pairs by paraphrasing or back-translation to diversify the positive examples.\\n\\n### 2. **If your pairs only indicate relatedness (no explicit \"not related\") label:**\\n\\n- Consider **Soft Margin Triplet Loss** or **Triplet Loss with in-batch negatives** by forming triplets automatically from the data.\\n\\n### 3. **If no negative pairs are available at all:**\\n\\n- Some unsupervised losses like **SimCLR style contrastive learning** rely only on positive pairs (augmented views) and use in-batch negatives implicitly.\\n\\n### Practical tip using Sentence Transformers library\\n\\nIf you use the [sentence-transformers library](https://www.sbert.net/docs/training/overview.html):\\n\\n- Use `InputExample(texts=[sentence1, sentence2])` for your positive pairs.\\n- Use `MultipleNegativesRankingLoss(model)` during training.\\n  \\nThis loss expects positive pairs and treats all other pairs in the batch as negatives automatically.\\n\\n---\\n\\n### Summary\\n\\n**If you only have pairs of related sentences, use a \"Multiple Negatives Ranking Loss\" with in-batch negatives.** This requires no explicit negative examples and is the standard approach for training sentence transformers with only positive pairs.\\n\\n---\\n\\n**References:**\\n\\n- [Sentence Transformers Training Overview](https://www.sbert.net/docs/training/overview.html)\\n- [Multiple Negatives Ranking Loss explanation](https://www.sbert.net/docs/package_reference/losses.html#sentence_transformers.losses.MultipleNegativesRankingLoss)\\n- [SimCSE paper (contrastive learning for sentence embeddings)](https://arxiv.org/abs/2104.08821)\\n\\n---\\n\\nIf you want, I can also help you with example code for this training setup!'"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# First let's make it simpler to get answers\n",
        "def complete(prompt):\n",
        "    response = client.chat.completions.create(\n",
        "        model=MODEL,\n",
        "        messages=[\n",
        "            {\n",
        "                \"role\": \"user\", \n",
        "                \"content\": prompt\n",
        "            }\n",
        "        ]\n",
        "    )\n",
        "    return response.choices[0].message.content\n",
        "\n",
        "query = (\n",
        "    \"Which training method should I use for sentence transformers when \" +\n",
        "    \"I only have pairs of related sentences?\"\n",
        ")\n",
        "\n",
        "complete(query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8G83i3cE6VNx"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Embedding(embedding=[-0.0006560460315085948, 0.017824385315179825, 0.028480269014835358, -0.016606571152806282, -0.044727031141519547, -0.03373901546001434, 0.02431478723883629, -0.015485626645386219, 0.014766008593142033, -0.006019881926476955, 0.03415418043732643, -0.010289154015481472, 0.0047017354518175125, -0.006542297080159187, 0.0650423988699913, 0.07246000319719315, -0.004393821582198143, 0.013312933966517448, -0.026805773377418518, 0.03683890774846077, 0.004642920568585396, 0.04671981930732727, -0.021422475576400757, 0.03445863351225853, 0.012710945680737495, -0.02185148000717163, -0.04054770991206169, -0.02493753284215927, 0.05117591470479965, -0.06244070455431938, -0.018557842820882797, -0.041239649057388306, 0.019125234335660934, -0.03044538013637066, -0.040160223841667175, 0.05463561788201332, 0.06559595465660095, 0.003992496524006128, -0.049792032688856125, -0.04818673059344292, -0.00959029421210289, -0.01252412237226963, 0.054884716868400574, 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            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Use OpenAI's text embedding model\n",
        "embed_model = \"text-embedding-3-small\"\n",
        "\n",
        "res = client.embeddings.create(\n",
        "    input=[\n",
        "        \"Sample document text goes here\",\n",
        "        \"there will be several phrases in each batch\"\n",
        "    ],\n",
        "    model=embed_model\n",
        ")\n",
        "\n",
        "# Vector embeddings for each document\n",
        "res.data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PG76DgVx6Ykp",
        "outputId": "e61cb608-d843-4a6e-c40f-64f6b94a3dd5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# We have created two vectors (one for each sentence input)\n",
        "len(res.data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoCcRCSz6bDh",
        "outputId": "266a6389-1ab2-42bc-a279-8450f4964c6c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1536, 1536)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# We have created two 1536-dimensional vectors\n",
        "len(res.data[0].embedding), len(res.data[1].embedding)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nsDlqGVeQ1NH",
        "outputId": "6ee49603-8df6-494b-b895-f5782ea9b618"
      },
      "outputs": [
        {
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              " ...]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# We can also get the vector for a single sentence\n",
        "res.data[0].embedding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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          ]
        },
        "id": "xE9yGmGc6dnJ",
        "outputId": "41d1ce5a-be8c-4788-e9c3-fd16fb18c652"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f4b84b3540434fbaa3366a8eea7a9d09",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/2.13k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "0.01s - Debugger warning: It seems that frozen modules are being used, which may\n",
            "0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off\n",
            "0.00s - to python to disable frozen modules.\n",
            "0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0e16758d5ec7407aacaef280d09c8599",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "youtube-transcriptions.jsonl:   0%|          | 0.00/79.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ada62a9c77e84b7ab39e111604704140",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/208619 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['title', 'published', 'url', 'video_id', 'channel_id', 'id', 'text', 'start', 'end'],\n",
              "    num_rows: 208619\n",
              "})"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset('jamescalam/youtube-transcriptions', split='train')\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l2flD74a6fCZ",
        "outputId": "8e9b8e56-a5a4-41c1-eede-e660caabf6d3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'title': 'Training and Testing an Italian BERT - Transformers From Scratch #4',\n",
              " 'published': '2021-07-06 13:00:03 UTC',\n",
              " 'url': 'https://youtu.be/35Pdoyi6ZoQ',\n",
              " 'video_id': '35Pdoyi6ZoQ',\n",
              " 'channel_id': 'UCv83tO5cePwHMt1952IVVHw',\n",
              " 'id': '35Pdoyi6ZoQ-t0.0',\n",
              " 'text': 'Hi, welcome to the video.',\n",
              " 'start': 0.0,\n",
              " 'end': 9.36}"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d2dd11c971904a19af7747739b4a7cb7",
            "d98787c25c754f4cb7a79fabfdd06281",
            "b34c3400d5dc4fa79e4ae713425cd935",
            "29716a238434474c8c1856d7f9c916f1",
            "06e0d5a0faee450e98ff2ca71b6a14f3",
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            "76a83a54d54d4660ada2b1e8d5faeb0d",
            "988815a1d9e84472a3cda6f61da1538c"
          ]
        },
        "id": "vqZcBAWg6gtB",
        "outputId": "9e792e29-7fd2-4d03-9b1b-df9ea3823660"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43fad9647ca44e33869f457a6b5fbc90",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/52155 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "new_data = []\n",
        "\n",
        "window = 20  # number of sentences to combine\n",
        "stride = 4  # number of sentences to 'stride' over, used to create overlap\n",
        "\n",
        "for i in tqdm(range(0, len(data), stride)):\n",
        "    i_end = min(len(data)-1, i+window)\n",
        "    if data[i]['title'] != data[i_end]['title']:\n",
        "        # in this case we skip this entry as we have start/end of two videos\n",
        "        continue\n",
        "    text = ' '.join(data[i:i_end]['text'])\n",
        "    # create the new merged dataset\n",
        "    new_data.append({\n",
        "        'start': data[i]['start'],\n",
        "        'end': data[i_end]['end'],\n",
        "        'title': data[i]['title'],\n",
        "        'text': text,\n",
        "        'id': data[i]['id'],\n",
        "        'url': data[i]['url'],\n",
        "        'published': data[i]['published'],\n",
        "        'channel_id': data[i]['channel_id']\n",
        "    })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFj8AH5E6ibZ",
        "outputId": "f328c213-98ca-4248-da7f-be1c657d4603"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'start': 0.0,\n",
              " 'end': 74.12,\n",
              " 'title': 'Training and Testing an Italian BERT - Transformers From Scratch #4',\n",
              " 'text': \"Hi, welcome to the video. So this is the fourth video in a Transformers from Scratch mini series. So if you haven't been following along, we've essentially covered what you can see on the screen. So we got some data. We built a tokenizer with it. And then we've set up our input pipeline ready to begin actually training our model, which is what we're going to cover in this video. So let's move over to the code. And we see here that we have essentially everything we've done so far. So we've built our input data, our input pipeline. And we're now at a point where we have a data loader, PyTorch data loader, ready. And we can begin training a model with it. So there are a few things to be aware of. So I mean, first, let's just have a quick look at the structure of our data.\",\n",
              " 'id': '35Pdoyi6ZoQ-t0.0',\n",
              " 'url': 'https://youtu.be/35Pdoyi6ZoQ',\n",
              " 'published': '2021-07-06 13:00:03 UTC',\n",
              " 'channel_id': 'UCv83tO5cePwHMt1952IVVHw'}"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LtvqPDnF6rlw",
        "outputId": "9b65c111-53ed-466d-ef55-b30e9e94fd60"
      },
      "outputs": [],
      "source": [
        "from pinecone import Pinecone, ServerlessSpec\n",
        "\n",
        "import os\n",
        "\n",
        "PINECONE_API_KEY = getpass.getpass(\"Please enter your pinecone key: \")\n",
        "\n",
        "# Initialize connection (get API key at app.pinecone.io):\n",
        "os.environ[\"PINECONE_API_KEY\"] = PINECONE_API_KEY"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suHuS4Tw6lFJ",
        "outputId": "670b2e3c-c767-4d7f-92ab-d1ac6aec7ddd"
      },
      "outputs": [],
      "source": [
        "index_name = \"employee-handbook\"\n",
        "pc = Pinecone()  # This reads the PINECONE_API_KEY env var\n",
        "\n",
        "if index_name not in pc.list_indexes().names():\n",
        "    pc.create_index(\n",
        "        name=index_name,\n",
        "        dimension=1536,  # Using the same vector dimensions as text-embedding-ada-002\n",
        "        metric=\"cosine\",\n",
        "        spec=ServerlessSpec(\n",
        "            cloud=\"aws\",\n",
        "            region=\"us-east-1\"\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Connect to Index:\n",
        "index = pc.Index(name=index_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'deletion_protection': 'disabled',\n",
            " 'dimension': 1536,\n",
            " 'host': 'employee-handbook-2321907.svc.aped-4627-b74a.pinecone.io',\n",
            " 'metric': 'cosine',\n",
            " 'name': 'employee-handbook',\n",
            " 'spec': {'serverless': {'cloud': 'aws', 'region': 'us-east-1'}},\n",
            " 'status': {'ready': False, 'state': 'Initializing'},\n",
            " 'tags': None,\n",
            " 'vector_type': 'dense'}\n"
          ]
        }
      ],
      "source": [
        "# Describe the Index:\n",
        "description = pc.describe_index(name=index_name)\n",
        "print(description)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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          ]
        },
        "id": "fGMbYLNP6y4J",
        "outputId": "8dc031e8-cf88-46f0-8703-23bf35f3904f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "07bc8033ef6d41d0988f35cb2fe79653",
              "version_major": 2,
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            },
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              "  0%|          | 0/487 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "import datetime\n",
        "import time\n",
        "from time import sleep\n",
        "\n",
        "# Wait for the index to be ready\n",
        "while not pc.describe_index(index_name).status['ready']:\n",
        "    time.sleep(1)\n",
        "\n",
        "batch_size = 100  # how many embeddings we create and insert at once\n",
        "\n",
        "for i in tqdm(range(0, len(new_data), batch_size)):\n",
        "    # find end of batch\n",
        "    i_end = min(len(new_data), i+batch_size)\n",
        "    meta_batch = new_data[i:i_end]\n",
        "    \n",
        "    # get texts to encode\n",
        "    texts = [x['text'] for x in meta_batch]\n",
        "    \n",
        "    # create embeddings (try-except added to avoid RateLimitError)\n",
        "    try:\n",
        "        res = client.embeddings.create(input=texts, model=embed_model)\n",
        "    except:\n",
        "        done = False\n",
        "        while not done:\n",
        "            sleep(5)\n",
        "            try:\n",
        "                res = client.embeddings.create(input=texts, model=embed_model)\n",
        "                done = True\n",
        "            except:\n",
        "                pass\n",
        "                \n",
        "    # prepare vectors for upsert\n",
        "    vectors = []\n",
        "    for data, embedding in zip(meta_batch, res.data):\n",
        "        vectors.append({\n",
        "            \"id\": data['id'],\n",
        "            \"values\": embedding.embedding,\n",
        "            \"metadata\": {\n",
        "                'start': data['start'],\n",
        "                'end': data['end'], \n",
        "                'title': data['title'],\n",
        "                'text': data['text'],\n",
        "                'url': data['url'],\n",
        "                'published': data['published'],\n",
        "                'channel_id': data['channel_id']\n",
        "            }\n",
        "        })\n",
        "    \n",
        "    # Upsert to Pinecone\n",
        "    index.upsert(vectors=vectors, namespace=\"ns1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "hwcYwF71601h"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Search results:\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Match 1 (Score: 0.669)\n",
            "ID: pNvujJ1XyeQ-t418.88\n",
            "\n",
            "Metadata:\n",
            "  Title: Today Unsupervised Sentence Transformers, Tomorrow Skynet (how TSDAE works)\n",
            "  Time: 418.9s - 568.4s\n",
            "  URL: https://youtu.be/pNvujJ1XyeQ\n",
            "  Published: 2021-11-24 16:24:24 UTC\n",
            "\n",
            "Text:\n",
            "  pairs of related sentences you can go ahead and actually try training or fine-tuning using NLI with multiple negative ranking loss. If you don't have that fine. Another option is that you have a semantic textual similarity data set or STS and what this is is you have so you have sentence A here, sentence B here and then you have a score from from 0 to 1 that tells you the similarity between those two scores and you would train this using something like cosine similarity loss. Now if that's not an option and your focus or use case is on building a sentence transformer for another language where there is no current sentence transformer you can use multilingual parallel data. So what I mean by that is so parallel data just means translation pairs so if you have for example a English sentence and then you have another language here so it can it can be anything I'm just going to put XX and that XX is your target language you can fine-tune a model using something called multilingual knowledge distillation and what that does is takes a monolingual model for example in English and using those translation pairs it distills the knowledge the semantic similarity knowledge from that monolingual English model into a multilingual model which can handle both English and your target language. So they're three options quite popular very common that you can go for and as a supervised methods the chances are that probably going to outperform anything you do with unsupervised training at least for now. So if none of those sound like something\n",
            "\n",
            "Match 2 (Score: 0.654)\n",
            "ID: pNvujJ1XyeQ-t165.48\n",
            "\n",
            "Metadata:\n",
            "  Title: Today Unsupervised Sentence Transformers, Tomorrow Skynet (how TSDAE works)\n",
            "  Time: 165.5s - 309.6s\n",
            "  URL: https://youtu.be/pNvujJ1XyeQ\n",
            "  Published: 2021-11-24 16:24:24 UTC\n",
            "\n",
            "Text:\n",
            "  In NLP transformers are the de facto standard and for building representations of sentences or paragraphs there is a subcategory of transformers called sentence transformers. Now the training process to build a transformer begins with something called pre-training that produces a generic transform model and then we fine-tune that so we train that further using special methods to build sentence transformers that can produce these very information rich and accurate sentence vectors. Now whereas pre-training tends to use unsupervised training methods fine-tuning tends to be more along the lines of supervised training and what that means is that we need a lot of labeled data and for some domains and languages there simply is not enough labeled data out there to actually build a sentence transformer for those specific domains or languages. So that means that you can always spend a long time gathering data and labeling all data to get tens of thousands of labeled samples or you can go ahead and try fine-tuning model using unsupervised training. Now unsupervised training I will tell you straight away is not going to get you the performance that you would get from a supervised training approach. However if you do not have the labeled data to train using a supervised approach, unsupervised training is your best bet and it still works pretty well. So in this video that's what we're going to cover we're going to cover how we can train a sentence transformer or fine-tune sentence transformer using a unsupervised training method called\n"
          ]
        }
      ],
      "source": [
        "res = client.embeddings.create(\n",
        "    input=[query],\n",
        "    model=embed_model\n",
        ")\n",
        "\n",
        "# retrieve from Pinecone\n",
        "xq = res.data[0].embedding\n",
        "\n",
        "# get relevant contexts (including the questions)\n",
        "res = index.query(namespace=\"ns1\", vector=xq, top_k=2, include_metadata=True)\n",
        "\n",
        "# Print search results in a readable format\n",
        "print(\"Search results:\")\n",
        "print(\"-\" * 80)\n",
        "for i, match in enumerate(res['matches'], 1):\n",
        "    print(f\"\\nMatch {i} (Score: {match['score']:.3f})\")\n",
        "    print(f\"ID: {match['id']}\")\n",
        "    print(\"\\nMetadata:\")\n",
        "    print(f\"  Title: {match['metadata']['title']}\")\n",
        "    print(f\"  Time: {match['metadata']['start']:.1f}s - {match['metadata']['end']:.1f}s\") \n",
        "    print(f\"  URL: {match['metadata']['url']}\")\n",
        "    print(f\"  Published: {match['metadata']['published']}\")\n",
        "    print(\"\\nText:\")\n",
        "    print(\"  \" + match['metadata']['text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "jeSJK-LC63gZ"
      },
      "outputs": [
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Answer the question based on the context below.\n",
            "\n",
            "Context:\n",
            "pairs of related sentences you can go ahead and actually try training or fine-tuning using NLI with multiple negative ranking loss. If you don't have that fine. Another option is that you have a semantic textual similarity data set or STS and what this is is you have so you have sentence A here, sentence B here and then you have a score from from 0 to 1 that tells you the similarity between those two scores and you would train this using something like cosine similarity loss. Now if that's not an option and your focus or use case is on building a sentence transformer for another language where there is no current sentence transformer you can use multilingual parallel data. So what I mean by that is so parallel data just means translation pairs so if you have for example a English sentence and then you have another language here so it can it can be anything I'm just going to put XX and that XX is your target language you can fine-tune a model using something called multilingual knowledge distillation and what that does is takes a monolingual model for example in English and using those translation pairs it distills the knowledge the semantic similarity knowledge from that monolingual English model into a multilingual model which can handle both English and your target language. So they're three options quite popular very common that you can go for and as a supervised methods the chances are that probably going to outperform anything you do with unsupervised training at least for now. So if none of those sound like something\n",
            "\n",
            "---\n",
            "\n",
            "In NLP transformers are the de facto standard and for building representations of sentences or paragraphs there is a subcategory of transformers called sentence transformers. Now the training process to build a transformer begins with something called pre-training that produces a generic transform model and then we fine-tune that so we train that further using special methods to build sentence transformers that can produce these very information rich and accurate sentence vectors. Now whereas pre-training tends to use unsupervised training methods fine-tuning tends to be more along the lines of supervised training and what that means is that we need a lot of labeled data and for some domains and languages there simply is not enough labeled data out there to actually build a sentence transformer for those specific domains or languages. So that means that you can always spend a long time gathering data and labeling all data to get tens of thousands of labeled samples or you can go ahead and try fine-tuning model using unsupervised training. Now unsupervised training I will tell you straight away is not going to get you the performance that you would get from a supervised training approach. However if you do not have the labeled data to train using a supervised approach, unsupervised training is your best bet and it still works pretty well. So in this video that's what we're going to cover we're going to cover how we can train a sentence transformer or fine-tune sentence transformer using a unsupervised training method called\n",
            "\n",
            "Question: Which training method should I use for sentence transformers when I only have pairs of related sentences?\n",
            "Answer:\n"
          ]
        }
      ],
      "source": [
        "limit = 3750\n",
        "\n",
        "def retrieve(query):\n",
        "    res = client.embeddings.create(\n",
        "        input=[query], \n",
        "        model=embed_model\n",
        "    )\n",
        "\n",
        "    # retrieve from Pinecone\n",
        "    xq = res.data[0].embedding\n",
        "\n",
        "    # get relevant contexts\n",
        "    res = index.query(namespace=\"ns1\", vector=xq, top_k=3, include_metadata=True)\n",
        "    contexts = [\n",
        "        x['metadata']['text'] for x in res['matches']\n",
        "    ]\n",
        "\n",
        "    # build our prompt with the retrieved contexts included\n",
        "    prompt_start = (\n",
        "        \"Answer the question based on the context below.\\n\\n\"+\n",
        "        \"Context:\\n\"\n",
        "    )\n",
        "    prompt_end = (\n",
        "        f\"\\n\\nQuestion: {query}\\nAnswer:\"\n",
        "    )\n",
        "    \n",
        "    # Initialize prompt with all contexts\n",
        "    prompt = (\n",
        "        prompt_start +\n",
        "        \"\\n\\n---\\n\\n\".join(contexts) + \n",
        "        prompt_end\n",
        "    )\n",
        "    \n",
        "    # If total length exceeds limit, reduce contexts one by one\n",
        "    for i in range(len(contexts)-1, 0, -1):\n",
        "        if len(\"\\n\\n---\\n\\n\".join(contexts[:i])) < limit:\n",
        "            prompt = (\n",
        "                prompt_start +\n",
        "                \"\\n\\n---\\n\\n\".join(contexts[:i]) +\n",
        "                prompt_end\n",
        "            )\n",
        "            break\n",
        "            \n",
        "    return prompt\n",
        "\n",
        "# First we retrieve relevant items from Pinecone\n",
        "query_with_contexts = retrieve(query)\n",
        "print(query_with_contexts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "SeySU_cB687P",
        "outputId": "d048754e-d544-4b4f-baa3-b71f4b269a04"
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      "outputs": [
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          "output_type": "stream",
          "text": [
            "If you only have pairs of related sentences, you should go ahead and try training or fine-tuning your sentence transformer using Natural Language Inference (NLI) data with multiple negative ranking loss. This supervised method leverages pairs of related sentences effectively and is one of the common approaches to fine-tuning sentence transformers when labeled data in that format is available.\n"
          ]
        }
      ],
      "source": [
        "# Then we complete the context-infused query\n",
        "print(complete(query_with_contexts))"
      ]
    }
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