double-layered-rag-benchmarks/rageval.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.3/309.3 kB\u001b[0m \u001b[31m12.8 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[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m5.5 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[31m5.1 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[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m50.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\n",
            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.2 which is incompatible.\n",
            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.27.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qdrant_client import models\n",
        "from langchain_community.document_loaders.url import UnstructuredURLLoader\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "\n",
        "\n",
        "def urlload(urls):\n",
        "    links = urls.split(\",\")\n",
        "    try:\n",
        "        loader = UnstructuredURLLoader(\n",
        "            urls=links, method=\"elements\",\n",
        "            strategy=\"fast\"\n",
        "        )\n",
        "        docs = loader.load()\n",
        "        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
        "        pages = text_splitter.split_documents(docs)\n",
        "        contents = [{\"text\": pages[i].page_content, \"url\": pages[i].metadata[\"source\"]} for i in range(len(pages))]\n",
        "        return contents\n",
        "    except Exception as e:\n",
        "        return f\"An error occurred while parsing the URLs: {e}\"\n",
        "\n",
        "class NeuralSearcher:\n",
        "    def __init__(self, collection_name, client, model):\n",
        "        self.collection_name = collection_name\n",
        "        # Initialize encoder model\n",
        "        self.model = model\n",
        "        # initialize Qdrant client\n",
        "        self.qdrant_client = client\n",
        "    def search(self, text: str, num_return: int):\n",
        "        # Convert text query into vector\n",
        "        vector = self.model.encode(text).tolist()\n",
        "\n",
        "        # Use `vector` for search for closest vectors in the collection\n",
        "        search_result = self.qdrant_client.search(\n",
        "            collection_name=self.collection_name,\n",
        "            query_vector=vector,\n",
        "            query_filter=None,  # If you don't want any filters for now\n",
        "            limit=num_return,  # 5 the most closest results is enough\n",
        "        )\n",
        "        # `search_result` contains found vector ids with similarity scores along with the stored payload\n",
        "        # In this function you are interested in payload only\n",
        "        payloads = [hit.payload for hit in search_result]\n",
        "        return payloads\n",
        "\n",
        "\n",
        "\n",
        "def upload_to_qdrant_collection(client, collection_name, encoder, documents):\n",
        "    client.upload_points(\n",
        "        collection_name=collection_name,\n",
        "        points=[\n",
        "            models.PointStruct(\n",
        "                id=idx, vector=encoder.encode(doc).tolist(), payload={\"text\": doc}\n",
        "            )\n",
        "            for idx, doc in enumerate(documents)\n",
        "        ],\n",
        "    )\n",
        "\n",
        "def upload_to_qdrant_subcollection(client, collection_name, encoder, documents):\n",
        "    client.delete_collection(collection_name=collection_name)\n",
        "    client.create_collection(collection_name = collection_name,\n",
        "        vectors_config=models.VectorParams(\n",
        "            size = encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model\n",
        "            distance = models.Distance.COSINE,\n",
        "        ),\n",
        "    )\n",
        "    client.upload_points(\n",
        "        collection_name=collection_name,\n",
        "        points=[\n",
        "            models.PointStruct(\n",
        "                id=idx, vector=encoder.encode(doc[\"text\"]).tolist(), payload=doc\n",
        "            )\n",
        "            for idx, doc in enumerate(documents)\n",
        "        ],\n",
        "    )\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-14T16:42:54.820405Z",
          "iopub.execute_input": "2024-07-14T16:42:54.820797Z",
          "iopub.status.idle": "2024-07-14T16:42:54.834809Z",
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          "shell.execute_reply": "2024-07-14T16:42:54.833566Z"
        },
        "trusted": true,
        "id": "Uft6xib-KEKc"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "from google.colab import userdata\n",
        "\n",
        "\n",
        "token = userdata.get(\"HF_TOKEN\")\n",
        "login(token)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-14T16:22:27.201719Z",
          "iopub.execute_input": "2024-07-14T16:22:27.202106Z",
          "iopub.status.idle": "2024-07-14T16:22:27.742075Z",
          "shell.execute_reply.started": "2024-07-14T16:22:27.202074Z",
          "shell.execute_reply": "2024-07-14T16:22:27.741109Z"
        },
        "trusted": true,
        "id": "s9_FhDt_KEKe",
        "outputId": "e75729e9-0f7f-43a6-e151-e3fc44053b5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: fineGrained).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"Lambent/synthetic-rag-hermes-simple-qa-1st-ic\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-14T16:24:14.518607Z",
          "iopub.execute_input": "2024-07-14T16:24:14.519008Z",
          "iopub.status.idle": "2024-07-14T16:24:16.594408Z",
          "shell.execute_reply.started": "2024-07-14T16:24:14.518974Z",
          "shell.execute_reply": "2024-07-14T16:24:16.593271Z"
        },
        "trusted": true,
        "id": "oJ709D7gKEKf",
        "outputId": "e8bf2088-d585-4dc0-a170-976aa0632b8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "06dc3bd2af8447088169eec6e911acb6"
          ]
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/2.17M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2d078a9db3d461d9d8baad56b9df106"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/358 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e7db1c64260484b94561455eeeab22f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "questions = dataset[\"train\"][\"instruction\"]\n",
        "answers = dataset[\"train\"][\"response\"]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-14T16:25:14.140924Z",
          "iopub.execute_input": "2024-07-14T16:25:14.141314Z",
          "iopub.status.idle": "2024-07-14T16:25:14.14883Z",
          "shell.execute_reply.started": "2024-07-14T16:25:14.14127Z",
          "shell.execute_reply": "2024-07-14T16:25:14.147573Z"
        },
        "trusted": true,
        "id": "ax4p_9F5KEKg"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from qdrant_client import QdrantClient, models\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "qdrant_api_key = userdata.get(\"QDRANT_API_KEY\")\n",
        "qdrant_db = userdata.get(\"QDRANT_DB\")\n",
        "\n",
        "client = QdrantClient(\n",
        "    url= qdrant_db,\n",
        "    api_key= qdrant_api_key,\n",
        ")\n",
        "\n",
        "encoder = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
        "encoder1 = SentenceTransformer(\"sentence-t5-base\")\n",
        "coll_name = \"Small_HTML_collection\"\n",
        "coll_name1 = \"HTML_collection\"\n",
        "subcoll_name = \"Subcollection\"\n",
        "small_subcoll_name = \"Small_Subcollection\"\n",
        "\n",
        "\n",
        "client.recreate_collection(\n",
        "    collection_name = coll_name,\n",
        "    vectors_config=models.VectorParams(\n",
        "        size = encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model\n",
        "        distance = models.Distance.COSINE,\n",
        "    ),\n",
        ")\n",
        "\n",
        "client.recreate_collection(\n",
        "    collection_name = coll_name1,\n",
        "    vectors_config=models.VectorParams(\n",
        "        size = encoder1.get_sentence_embedding_dimension(),  # Vector size is defined by used model\n",
        "        distance = models.Distance.COSINE,\n",
        "    ),\n",
        ")\n",
        "\n",
        "\n",
        "client.recreate_collection(\n",
        "    collection_name = subcoll_name,\n",
        "    vectors_config=models.VectorParams(\n",
        "        size = encoder1.get_sentence_embedding_dimension(),  # Vector size is defined by used model\n",
        "        distance = models.Distance.COSINE,\n",
        "    ),\n",
        ")\n",
        "\n",
        "client.recreate_collection(\n",
        "    collection_name = small_subcoll_name,\n",
        "    vectors_config=models.VectorParams(\n",
        "        size = encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model\n",
        "        distance = models.Distance.COSINE,\n",
        "    ),\n",
        ")\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-14T16:35:48.342701Z",
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            "<ipython-input-6-245ea8318083>:20: DeprecationWarning: `recreate_collection` method is deprecated and will be removed in the future. Use `collection_exists` to check collection existence and `create_collection` instead.\n",
            "  client.recreate_collection(\n",
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            "  client.recreate_collection(\n",
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            "  client.recreate_collection(\n",
            "<ipython-input-6-245ea8318083>:45: DeprecationWarning: `recreate_collection` method is deprecated and will be removed in the future. Use `collection_exists` to check collection existence and `create_collection` instead.\n",
            "  client.recreate_collection(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def call_upload2qdrant(documents, coll_name, encoder):\n",
        "    global client\n",
        "    if type(documents) == list:\n",
        "        try:\n",
        "            upload_to_qdrant_collection(client, coll_name, encoder, documents)\n",
        "            return \"Successfully uploaded URL content to Qdrant collection\"\n",
        "        except Exception as e:\n",
        "            return f\"An error occured: {e}\"\n",
        "    else:\n",
        "        return documents\n",
        "\n",
        "def reranked_rag(client, encoder0, encoder1, collection0, collection1, message):\n",
        "    txt2txt0 = NeuralSearcher(collection0, client, encoder0)\n",
        "    search_results0 = txt2txt0.search(message, 10)\n",
        "    upload_to_qdrant_subcollection(client, collection1, encoder1, search_results0)\n",
        "    txt2txt1 = NeuralSearcher(collection1, client, encoder1)\n",
        "    search_results1 = txt2txt1.search(message, 1)\n",
        "    return search_results1\n",
        "\n",
        "def direct_search(input_text):\n",
        "    global client, encoder, encoder1, coll_name, subcoll_name\n",
        "    results = reranked_rag(client, encoder, encoder1, coll_name, subcoll_name, input_text)\n",
        "    return results[0][\"text\"]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-14T16:37:15.164697Z",
          "iopub.execute_input": "2024-07-14T16:37:15.165448Z",
          "iopub.status.idle": "2024-07-14T16:37:15.177743Z",
          "shell.execute_reply.started": "2024-07-14T16:37:15.165412Z",
          "shell.execute_reply": "2024-07-14T16:37:15.176554Z"
        },
        "trusted": true,
        "id": "wf45d9LlKEKh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__==\"__main__\":\n",
        "    docs = answers\n",
        "\n",
        "    print(call_upload2qdrant(docs,coll_name,encoder))\n",
        "\n",
        "    print(call_upload2qdrant(docs,coll_name1,encoder1))\n",
        "\n",
        "    from math import ceil\n",
        "    import time\n",
        "\n",
        "    newdocs = {}\n",
        "    for i in range(len(docs)):\n",
        "        newdocs.update({questions[i]: docs[i]})\n",
        "\n",
        "    print(\"Successful mapping\")\n",
        "    txt2txt0 = NeuralSearcher(coll_name, client, encoder)\n",
        "    txt2txt1 = NeuralSearcher(coll_name1, client, encoder1)\n",
        "\n",
        "    times0 = []\n",
        "    times1 = []\n",
        "    times01 = []\n",
        "    times10 = []\n",
        "    points0 = 0\n",
        "    points1 = 0\n",
        "    points01 = 0\n",
        "    points10 = 0\n",
        "\n",
        "    from statistics import mean, stdev\n",
        "\n",
        "    print(\"Started benchmark\")\n",
        "    for k in newdocs:\n",
        "        start0 = time.time()\n",
        "        results0 = txt2txt0.search(k,1)\n",
        "        end0 = time.time()\n",
        "        if results0[0][\"text\"] == newdocs[k]:\n",
        "            points0+=1\n",
        "        times0.append(end0-start0)\n",
        "        start1 = time.time()\n",
        "        results1 = txt2txt1.search(k,1)\n",
        "        end1 = time.time()\n",
        "        if results1[0][\"text\"] == newdocs[k]:\n",
        "            points1+=1\n",
        "        times1.append(end1-start1)\n",
        "        start01 = time.time()\n",
        "        results01 = reranked_rag(client, encoder, encoder1, coll_name, subcoll_name, k)\n",
        "        end01 = time.time()\n",
        "        if results01[0][\"text\"] == newdocs[k]:\n",
        "            points01+=1\n",
        "        times01.append(end01-start01)\n",
        "        start10 = time.time()\n",
        "        results10 = reranked_rag(client, encoder1, encoder, coll_name1, small_subcoll_name, k)\n",
        "        end10 = time.time()\n",
        "        if results10[0][\"text\"] == newdocs[k]:\n",
        "            points10+=1\n",
        "        times10.append(end10-start10)\n",
        "\n",
        "    print(f\"Avg time for All-MiniLM-L6-v2: {mean(times0)} +/- {stdev(times0)}\")\n",
        "    print(f\"Avg time for sentence-t5-base: {mean(times1)} +/- {stdev(times1)}\")\n",
        "    print(f\"Avg time for All-MiniLM-L6-v2 + sentence-t5-base: {mean(times01)} +/- {stdev(times01)}\")\n",
        "    print(f\"Avg time for sentence-t5-base + All-MiniLM-L6-v2: {mean(times10)} +/- {stdev(times10)}\")\n",
        "    print(f\"Correct/Total retrievals for All-MiniLM-L6-v2: {points0/len(newdocs)}\")\n",
        "    print(f\"Correct/Total retrievals for sentence-t5-base: {points1/len(newdocs)}\")\n",
        "    print(f\"Correct/Total retrievals for All-MiniLM-L6-v2 + sentence-t5-base: {points01/len(newdocs)}\")\n",
        "    print(f\"Correct/Total retrievals for sentence-t5-base + All-MiniLM-L6-v2: {points10/len(newdocs)}\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-14T16:43:14.576276Z",
          "iopub.execute_input": "2024-07-14T16:43:14.576672Z"
        },
        "trusted": true,
        "id": "DpNHkwFbKEKi",
        "outputId": "18d45d0f-3407-43f0-a343-0cd1a2d9d22b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully uploaded URL content to Qdrant collection\n",
            "Successfully uploaded URL content to Qdrant collection\n",
            "Successful mapping\n",
            "Started benchmark\n",
            "Avg time for All-MiniLM-L6-v2: 0.13889479704117508 +/- 0.018010675079187972\n",
            "Avg time for sentence-t5-base: 0.3546350625123871 +/- 0.1378480017367839\n",
            "Avg time for All-MiniLM-L6-v2 + sentence-t5-base: 10.722357098306164 +/- 1.2552639024596886\n",
            "Avg time for sentence-t5-base + All-MiniLM-L6-v2: 2.722710152020615 +/- 0.30703480688834284\n",
            "Correct/Total retrievals for All-MiniLM-L6-v2: 0.41853932584269665\n",
            "Correct/Total retrievals for sentence-t5-base: 0.5084269662921348\n",
            "Correct/Total retrievals for All-MiniLM-L6-v2 + sentence-t5-base: 0.5028089887640449\n",
            "Correct/Total retrievals for sentence-t5-base + All-MiniLM-L6-v2: 0.42134831460674155\n"
          ]
        }
      ]
    }
  ]
}