{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexabades/DocuRAG/Api/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from qdrant_client import QdrantClient\n",
    "from qdrant_client.models import Distance, PointStruct, VectorParams\n",
    "from qdrant_client.models import PointStruct, VectorParams\n",
    "from qdrant_client import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseURL = \"http://localhost:6333\"\n",
    "# QDRANT_API_KEY=\"wRnRbEQU815UgyLrwVVrmwO80wLeodYW4fH1J12_dtg91a_uSwxV_Q\"\n",
    "client = QdrantClient(url=baseURL)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([Record(id='3d50ccd5-bce3-4f7e-a2e8-069a1916a9cb', payload={'document_id': 'Energy Trading', 'chunk_index': 2, 'filename': 'Energy Trading.pdf', 'chunk-text': 'on to technical skills, I have a collaborative mindset developed through my diverse\\nexperiences, including working closely with students and faculty at Technical University of\\nDenmark and collaborating with cross-functional teams at Incotec. This background has\\nequipped me to work effectively with traders, analysts, and stakeholders to ensure that platform\\nimprovements directly impact business outcomes.\\n\\n\\nI am particularly excited about the direct involvement of platform engineers in shaping\\nCopenhagen Ener'}, vector=None, shard_key=None, order_value=None),\n",
       "  Record(id='47d3a00a-f874-422f-b03f-3ed757a1ee2a', payload={'document_id': 'Energy Trading', 'chunk_index': 1, 'filename': 'Energy Trading.pdf', 'chunk-text': ' involving Python-based infrastructure, where I\\ndesigned and implemented scalable backend solutions using FastAPI and Node.js. I have a\\nproven ability to handle critical technological decisions, such as building data pipelines and\\nimproving backend processes that closely align with business objectives. Additionally, my\\nexperience with DevOps practices like automation and containerization (using Docker) has\\nstrengthened my ability to build and maintain efficient, high-performing software systems.\\n\\n\\nIn additi'}, vector=None, shard_key=None, order_value=None),\n",
       "  Record(id='ab9650e3-c738-439e-9a4c-9bfbe615ad5e', payload={'document_id': 'Energy Trading', 'chunk_index': 3, 'filename': 'Energy Trading.pdf', 'chunk-text': 'gy Trading’s technology and business strategies. The opportunity to apply my\\nknowledge of cloud infrastructure (Azure), Python, and DevOps aligns perfectly with my passion\\nfor creating impactful solutions in dynamic environments.\\n\\n\\nI would welcome the opportunity to further discuss how my background and enthusiasm for\\ninnovative technology can contribute to the continued growth of Copenhagen Energy Trading.\\n\\n\\nThank you for considering my application.\\n\\n\\nSincerely,\\n\\nAlex Abades'}, vector=None, shard_key=None, order_value=None),\n",
       "  Record(id='cd442689-d962-4c72-9594-9b154cfa592e', payload={'document_id': 'Energy Trading', 'chunk_index': 0, 'filename': 'Energy Trading.pdf', 'chunk-text': 'ALEX ABADES GRIMES\\nAI Engineer\\nDear Hiring Manager,\\n\\n\\nI am writing to express my interest in the Platform Engineer (Python) position at Copenhagen\\nEnergy Trading. With a Master’s in Human-Centered Artificial Intelligence from the Technical\\nUniversity of Denmark and hands-on experience as a co-founder of a tech startup, I am eager to\\nbring my skills in software development, Python programming, and cloud infrastructure to your\\nrapidly expanding energy trading firm.\\n\\n\\nAt NeoCareU, I co-founded and led projects'}, vector=None, shard_key=None, order_value=None)],\n",
       " None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection_name = \"testfile\"\n",
    "client.scroll(\n",
    "    collection_name=collection_name,\n",
    "    scroll_filter=models.Filter(\n",
    "        should=[\n",
    "            models.FieldCondition(key=\"document_id\", match=models.MatchValue(value=\"Energy Trading\"))\n",
    "        ]\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection_name = \"example_collection\"\n",
    "client.create_collection(\n",
    "    collection_name=collection_name,\n",
    "    vectors_config={\n",
    "        \"text-dense\": models.VectorParams(\n",
    "            size=1536,  # OpenAI Embeddings\n",
    "            distance=models.Distance.COSINE,\n",
    "        )\n",
    "    },\n",
    "    sparse_vectors_config={\n",
    "        \"text-sparse\": models.SparseVectorParams(\n",
    "            index=models.SparseIndexParams(\n",
    "                on_disk=False,\n",
    "            )\n",
    "        )\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UpdateResult(operation_id=0, status=<UpdateStatus.COMPLETED: 'completed'>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = [\n",
    "    PointStruct(\n",
    "        id=1,\n",
    "        vector={\n",
    "            \"text-dense\": [0.1] * 1536,  # Dense vector\n",
    "            \"text-sparse\": models.SparseVector(indices=[6, 7], values=[1.0, 2.0]),\n",
    "        },\n",
    "        payload={\"filename\": f\"test\"},  # Metadata if needed\n",
    "    ),\n",
    "    PointStruct(\n",
    "        id=2,\n",
    "        vector={\n",
    "            \"text-dense\": [0.101] * 1536,  # Dense vector\n",
    "            \"text-sparse\": models.SparseVector(indices=[6, 7], values=[1.0, 2.0]),\n",
    "        },\n",
    "        payload={\"filename\": f\"test\"},  # Metadata if needed\n",
    "    ),\n",
    "    PointStruct(\n",
    "        id=3,\n",
    "        vector={\n",
    "            \"text-dense\": [0.99] * 1536,  # Dense vector\n",
    "            \"text-sparse\": models.SparseVector(indices=[6, 7], values=[1.0, 2.0]),\n",
    "        },\n",
    "        payload={\"filename\": f\"point_{3}\"},  # Metadata if needed\n",
    "    ),\n",
    "]\n",
    "\n",
    "client.upsert(\n",
    "    collection_name=collection_name,\n",
    "    wait=True,\n",
    "    points=points,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = client.scroll(\n",
    "    collection_name=\"testfile\",\n",
    "    with_payload=True,\n",
    "    with_vectors=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tuple, list, NoneType)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(result), type(result[0]), type(result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qdrant_client.models import Record\n",
    "result2 = ([\n",
    "  Record(id='13eb202b-1829-4a01-aa7a-c4368cea9a2b', payload={'document_id': 'Kapa', 'chunk_index': 3, 'filename': 'Kapa.ai-Research.pdf', 'chunk-text': 'Some text kapa 1'}, vector=None, shard_key=None, order_value=None),\n",
    "  Record(id='13eb202b-1829-4a01-aa7a-c4368cea9a2b', payload={'document_id': 'Kapa', 'chunk_index': 1, 'filename': 'Kapa.ai-Research.pdf', 'chunk-text': 'Some text kapa 1'}, vector=None, shard_key=None, order_value=None),\n",
    "  Record(id='522c1c8a-2d89-4967-9ddc-65ec9016008a', payload={'document_id': 'Kapa', 'chunk_index': 0, 'filename': 'Kapa.ai-Research.pdf', 'chunk-text': 'Some text kapa '}, vector=None, shard_key=None, order_value=None),\n",
    "  Record(id='522c1c8a-2d89-4967-9ddc-65ec9016008a', payload={'document_id': 'Kapa', 'chunk_index': 2, 'filename': 'Kapa.ai-Research.pdf', 'chunk-text': 'Some text kapa '}, vector=None, shard_key=None, order_value=None),\n",
    "  Record(id='b0a25b6b-8f1e-4616-8211-4af067bdafac', payload={'document_id': 'Emagine', 'chunk_index': 3, 'filename': 'Emagine.pdf', 'chunk-text': 'Some text emagine '}, vector=None, shard_key=None, order_value=None),\n",
    "  Record(id='aa9d4554-cbe5-4e62-b1f1-a905869c98c5', payload={'document_id': 'Emagine', 'chunk_index': 0, 'filename': 'Emagine.pdf', 'chunk-text': 'Some text emagine '}, vector=None, shard_key=None, order_value=None),\n",
    "  Record(id='b0a25b6b-8f1e-4616-8211-4af067bdafac', payload={'document_id': 'Emagine', 'chunk_index': 1, 'filename': 'Emagine.pdf', 'chunk-text': 'Some text emagine '}, vector=None, shard_key=None, order_value=None),\n",
    "  Record(id='b0a25b6b-8f1e-4616-8211-4af067bdafac', payload={'document_id': 'Emagine', 'chunk_index': 2, 'filename': 'Emagine.pdf', 'chunk-text': 'Some text emagine '}, vector=None, shard_key=None, order_value=None),\n",
    "  ],\n",
    " None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, List\n",
    "from pydantic import BaseModel\n",
    "\n",
    "\n",
    "class ChunkData(BaseModel):\n",
    "    chunkText: str\n",
    "\n",
    "\n",
    "class DocumentData(BaseModel):\n",
    "    data: List[ChunkData]\n",
    "\n",
    "\n",
    "class ChunksResponse(BaseModel):\n",
    "    data: Dict[str, DocumentData]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data={'Kapa': DocumentData(data=[ChunkData(chunkText='Some text kapa 1')])}\n",
      "data={'Kapa': DocumentData(data=[ChunkData(chunkText='Some text kapa 2')])}\n",
      "data={'Emagine': DocumentData(data=[ChunkData(chunkText='Some text emagine 1')])}\n",
      "data={'Emagine': DocumentData(data=[ChunkData(chunkText='Some text emagine 2')])}\n"
     ]
    }
   ],
   "source": [
    "qdrant_response = result2\n",
    "\n",
    "for document_index, document_chunks in enumerate(qdrant_response[0], start=1):\n",
    "    print(\n",
    "        ChunksResponse(\n",
    "            data={\n",
    "                document_chunks.payload[\"document_id\"]: DocumentData(\n",
    "                    data=[ChunkData(chunkText=document_chunks.payload[\"chunk-text\"])]\n",
    "                )\n",
    "            }\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \"Kapa\": {\n",
    "        \"chunks\": [\n",
    "            \"This is a test\",\n",
    "            \"This is another test\",\n",
    "        ],\n",
    "    },\n",
    "    \"Emagine\": {\n",
    "        \"chunks\": [\n",
    "            \"This is a test\",\n",
    "            \"This is another test\",\n",
    "        ],\n",
    "    },\n",
    "}\n",
    "\n",
    "class documentChunks(BaseModel, str)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_data = {}\n",
    "qdrant_response = client.scroll(\n",
    "    collection_name=\"testfile\",\n",
    "    with_payload=True,\n",
    "    with_vectors=False,\n",
    ")\n",
    "\n",
    "\n",
    "for document_index, document_chunks in enumerate(qdrant_response[0], start=1):\n",
    "    try:\n",
    "        transformed_data[document_chunks.payload[\"document_id\"]].append(\n",
    "            {\n",
    "                document_chunks.payload[\"chunk_index\"]: document_chunks.payload[\n",
    "                    \"chunk-text\"\n",
    "                ]\n",
    "            }\n",
    "        )\n",
    "\n",
    "    except KeyError:\n",
    "        transformed_data[document_chunks.payload[\"document_id\"]] = [\n",
    "            {\n",
    "                document_chunks.payload[\"chunk_index\"]: document_chunks.payload[\n",
    "                    \"chunk-text\"\n",
    "                ]\n",
    "            }\n",
    "        ]\n",
    "\n",
    "\n",
    "for doc in transformed_data:\n",
    "    transformed_data[doc] = sorted(\n",
    "        transformed_data[doc], key=lambda x: list(x.keys())[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Kapa': [{0: 'Some text kapa 2'}, {1: 'Some text kapa 1'}],\n",
       " 'Emagine': [{0: 'Some text emagine 1'}, {1: 'Some text emagine 2'}]}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a model for each chunk\n",
    "class Chunk(BaseModel):\n",
    "    index: int\n",
    "    text: str\n",
    "\n",
    "\n",
    "# Define a model for the entire response\n",
    "class ChunksResponse(BaseModel):\n",
    "    data: Dict[str, List[Chunk]]\n",
    "\n",
    "\n",
    "transformed_data = {}\n",
    "\n",
    "for document in result2[0]:  # Assuming results[0] contains the Qdrant data\n",
    "    document_id = document.payload[\"document_id\"]\n",
    "    chunk_index = document.payload[\"chunk_index\"]\n",
    "    text = document.payload[\"chunk-text\"]\n",
    "\n",
    "    if document_id not in transformed_data:\n",
    "        transformed_data[document_id] = []\n",
    "\n",
    "    transformed_data[document_id].append({\"index\": chunk_index, \"text\": text})\n",
    "\n",
    "for doc in transformed_data:\n",
    "    transformed_data[doc] = sorted(transformed_data[doc], key=lambda x: x[\"index\"])\n",
    "result_transf = ChunksResponse(data=transformed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChunksResponse(data={'Kapa': [Chunk(index=0, text='Some text kapa '), Chunk(index=1, text='Some text kapa 1'), Chunk(index=2, text='Some text kapa '), Chunk(index=3, text='Some text kapa 1')], 'Emagine': [Chunk(index=0, text='Some text emagine '), Chunk(index=1, text='Some text emagine '), Chunk(index=2, text='Some text emagine '), Chunk(index=3, text='Some text emagine ')]})"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_transf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChunksResponse(data={'Kapa': [Chunk(index=3, text='Some text kapa 1'), Chunk(index=1, text='Some text kapa 1'), Chunk(index=0, text='Some text kapa '), Chunk(index=2, text='Some text kapa ')], 'Emagine': [Chunk(index=3, text='Some text emagine '), Chunk(index=0, text='Some text emagine '), Chunk(index=1, text='Some text emagine '), Chunk(index=2, text='Some text emagine ')]})"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ChunksResponse(\n",
    "    data={\n",
    "        \"Kapa\": [\n",
    "            Chunk(index=3, text=\"Some text kapa 1\"),\n",
    "            Chunk(index=1, text=\"Some text kapa 1\"),\n",
    "            Chunk(index=0, text=\"Some text kapa \"),\n",
    "            Chunk(index=2, text=\"Some text kapa \"),\n",
    "        ],\n",
    "        \"Emagine\": [\n",
    "            Chunk(index=3, text=\"Some text emagine \"),\n",
    "            Chunk(index=0, text=\"Some text emagine \"),\n",
    "            Chunk(index=1, text=\"Some text emagine \"),\n",
    "            Chunk(index=2, text=\"Some text emagine \"),\n",
    "        ],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \"Kapa\": [\n",
    "        {\n",
    "            0: \"Some text kapa Some text kapa Some text kapa Some text kapa Some text kapa Some text kapa Some text kapa \"\n",
    "        },\n",
    "        {\n",
    "            1: \"Some text kapa 1 Some text kapa Some text kapa Some text kapa Some text kapa Some text kapa Some text kapa Some text kapa \"\n",
    "        },\n",
    "    ],\n",
    "    \"Emagine\": [\n",
    "        {\n",
    "            0: \"Some text emagine 1 Some text emagine 1 Some text emagine 1 Some text emagine 1 Some text emagine 1 Some text emagine 1 \"\n",
    "        },\n",
    "        {\n",
    "            1: \"Some text emagine 2 Some text emagine 2 Some text emagine 2 Some text emagine 2 Some text emagine 2 Some text emagine 2 Some text emagine 2 \"\n",
    "        },\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Kapa': [{2: 'Some text kapa 2'}, {3: 'Some text kapa 1'}],\n",
       " 'Emagine': [{0: 'Some text emagine 1'}, {1: 'Some text emagine 2'}]}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformed_data[\"Kapa\"].sort(key=lambda x: list(x.keys())[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{0: 'ALEX ABADES GRIMES\\nAI Engineer\\nDear Finn & Emil,\\n\\n\\nI am writing to express my interest in the Research Engineer position at Kapa.ai. With a Master’s\\nin Human-Centered Artificial Intelligence from Denmark’s Technical University and a strong\\nbackground in machine learning, retrieval techniques, and AI product development, I am excited\\nabout the opportunity to contribute to Kapa’s mission of enhancing technical query resolution\\nthrough cutting-edge retrieval and machine learning techniques.\\n\\n\\nIn my previous ro'},\n",
       " {1: 'le as Co-Founder at NeoCareU, I led the end-to-end development of Python-\\nbased infrastructures, including implementing graph search functionalities using FastAPI, motor,\\nand uvicorn. This hands-on experience has provided me with a solid foundation in developing\\nrobust and scalable backend solutions. Additionally, I have experience working with retrieval-\\naugmented generation (RAG), which aligns closely with Kapa’s focus on improving technical\\nquery handling.\\n\\n\\nMy passion for retrieval-based AI technologies'},\n",
       " {2: ' is further reflected in my research projects. For\\ninstance, I’ve developed my own retrieval-augmented generation (RAG) system using natural\\nlanguage processing and vector databases. While this system is still evolving and not fully\\noptimized for search, it has allowed me to experiment with deploying machine learning models to\\ntackle complex data retrieval challenges. This hands-on experience would enable me to\\ncontribute effectively to Kapa’s mission of answering more sophisticated technical questions.\\n\\n\\nT'},\n",
       " {3: 'hank you for considering my application. I look forward to the opportunity to discuss how my\\nbackground in AI, deep learning, and search techniques aligns with the goals of Kapa.ai.\\n\\n\\nSincerely,\\n\\n\\nAlex Abades Grimes'}]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(transformed_data[\"Kapa\"], key=lambda x: list(x.keys())[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Kapa': [{3: 'hank you for considering my application. I look forward to the opportunity to discuss how my\\nbackground in AI, deep learning, and search techniques aligns with the goals of Kapa.ai.\\n\\n\\nSincerely,\\n\\n\\nAlex Abades Grimes'},\n",
       "  {2: ' is further reflected in my research projects. For\\ninstance, I’ve developed my own retrieval-augmented generation (RAG) system using natural\\nlanguage processing and vector databases. While this system is still evolving and not fully\\noptimized for search, it has allowed me to experiment with deploying machine learning models to\\ntackle complex data retrieval challenges. This hands-on experience would enable me to\\ncontribute effectively to Kapa’s mission of answering more sophisticated technical questions.\\n\\n\\nT'},\n",
       "  {0: 'ALEX ABADES GRIMES\\nAI Engineer\\nDear Finn & Emil,\\n\\n\\nI am writing to express my interest in the Research Engineer position at Kapa.ai. With a Master’s\\nin Human-Centered Artificial Intelligence from Denmark’s Technical University and a strong\\nbackground in machine learning, retrieval techniques, and AI product development, I am excited\\nabout the opportunity to contribute to Kapa’s mission of enhancing technical query resolution\\nthrough cutting-edge retrieval and machine learning techniques.\\n\\n\\nIn my previous ro'},\n",
       "  {1: 'le as Co-Founder at NeoCareU, I led the end-to-end development of Python-\\nbased infrastructures, including implementing graph search functionalities using FastAPI, motor,\\nand uvicorn. This hands-on experience has provided me with a solid foundation in developing\\nrobust and scalable backend solutions. Additionally, I have experience working with retrieval-\\naugmented generation (RAG), which aligns closely with Kapa’s focus on improving technical\\nquery handling.\\n\\n\\nMy passion for retrieval-based AI technologies'}]}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for document_index, document_chunks in enumerate(result[0], start=1):\n",
    "  print(document_chunks.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(qdrant_client.http.models.models.Record, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(result[0][0]), result[0][0].id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7, 0.04, 0.3, 0.06, 0.25, 0.88, 0.63, 0.18, 0.42, 0.77, 0.24, 0.96, 0.79, 0.67, 0.72, 0.71, 0.52, 0.16, 0.34, 0.72, 0.13, 0.09, 0.43, 0.74, 0.24, 0.9, 0.58, 0.2, 0.23, 0.43, 0.7, 0.46, 0.14, 0.98, 0.06, 0.39, 0.8, 0.96, 0.2, 0.68, 0.14, 0.65, 0.96, 0.88, 0.21, 0.5, 0.58, 0.76, 0.1, 0.71, 0.62, 0.38, 0.51, 0.35, 0.01, 0.51, 0.39, 0.09, 0.32, 0.31, 0.82, 0.5, 0.74, 0.05, 0.07, 0.25, 0.35, 0.44, 0.98, 0.3, 0.08, 0.08, 0.29, 0.21, 0.0, 0.36, 0.73, 0.82, 0.42, 0.92, 0.11, 0.2, 0.13, 0.32, 0.72, 0.2, 0.7, 0.01, 0.52, 0.86, 0.77, 0.53, 0.14, 0.31, 0.25, 0.23, 0.9, 0.66, 0.44, 0.64, 0.9, 0.26, 0.58, 0.94, 0.5, 0.93, 0.94, 0.57, 0.84, 0.47, 0.68, 0.37, 0.58, 0.35, 0.82, 0.71, 0.32, 0.78, 0.2, 0.27, 0.15, 0.52, 0.32, 0.57, 0.78, 0.42, 0.83, 0.21, 0.29, 0.78, 0.3, 0.97, 0.03, 0.96, 0.49, 0.75, 0.42, 0.9, 0.73, 0.13, 0.85, 0.57, 0.49, 0.85, 0.89, 0.91, 0.3, 0.97, 0.47, 1.0, 0.62, 0.44, 0.54, 0.85, 0.33, 0.25, 0.52, 0.84, 0.68, 0.75, 0.26, 0.01, 0.23, 0.55, 0.65, 0.8, 0.81, 0.54, 0.38, 0.5, 0.51, 0.25, 0.23, 0.84, 0.31, 0.35, 0.77, 0.44, 0.75, 0.22, 0.96, 0.79, 0.51, 0.64, 0.85, 0.66, 0.89, 0.96, 0.7, 0.95, 0.37, 0.72, 0.95, 0.38, 0.16, 0.49, 0.05, 0.76, 0.31, 0.71, 0.9, 0.25, 0.31, 0.9, 0.36, 0.73, 0.53, 0.2, 0.63, 0.6, 0.9, 0.76, 0.06, 0.72, 0.67, 0.86, 0.86, 0.47, 0.41, 0.31, 0.57, 0.89, 0.6, 0.04, 0.86, 0.91, 0.19, 0.38, 0.87, 0.5, 0.84, 0.34, 0.91, 0.92, 0.74, 0.55, 0.19, 0.18, 0.14, 0.57, 0.48, 0.77, 0.51, 0.97, 0.5, 0.07, 0.65, 0.53, 0.98, 0.12, 0.5, 0.07, 0.96, 0.96, 0.29, 0.62, 0.41, 0.36, 0.22, 0.88, 0.45, 0.41, 0.37, 0.49, 0.37, 0.82, 0.65, 0.08, 0.14, 0.42, 0.55, 0.95, 0.02, 0.32, 0.79, 0.27, 0.72, 0.15, 0.86, 0.36, 0.92, 0.35, 0.52, 0.06, 0.52, 0.23, 0.13, 0.36, 0.72, 0.62, 1.0, 0.94, 0.93, 0.68, 0.97, 0.72, 0.53, 0.74, 0.42, 0.42, 0.45, 0.37, 0.13, 0.92, 0.62, 0.19, 0.55, 0.28, 0.47, 0.09, 0.49, 0.17, 0.69, 0.0, 0.34, 0.13, 0.54, 0.69, 0.22, 0.87, 0.29, 0.79, 0.35, 0.12, 0.62, 0.57, 0.2, 0.27, 0.54, 0.57, 0.37, 0.6, 0.98, 0.52, 0.35, 0.44, 0.05, 0.38, 0.89, 0.7, 0.48, 0.58, 0.59, 0.8, 0.37, 0.39, 0.17, 0.35, 0.56, 0.51, 0.52, 0.46, 0.68, 0.83, 0.93, 0.62, 0.14, 0.65, 0.48, 0.72, 0.11, 0.47, 0.55, 0.39, 0.56, 0.26, 0.96, 0.99, 0.0, 0.77, 0.15, 0.98, 0.8, 0.96, 0.77, 0.01, 0.11, 0.23, 0.04, 0.82, 0.6, 0.29, 0.67, 0.09]\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "text_dense_vector = [round(random.random(),2) for _ in range(384)]\n",
    "print(text_dense_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "document_name = \"test\"\n",
    "result = client.scroll(\n",
    "            collection_name=collection_name,\n",
    "            scroll_filter=models.Filter(\n",
    "                must=[\n",
    "                    models.FieldCondition(\n",
    "                        key=\"filename\", match=models.MatchValue(value=document_name)\n",
    "                    )\n",
    "                ]\n",
    "            ),\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[point.id for point in result[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "if result[0]:\n",
    "  print(\"somethinf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "QdrantClient.search() missing 1 required positional argument: 'query_vector'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcollection_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollection_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquery_filter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFilter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmust\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFieldCondition\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m                \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfilename\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMatchValue\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdocument_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m        \u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: QdrantClient.search() missing 1 required positional argument: 'query_vector'"
     ]
    }
   ],
   "source": [
    "client.search(\n",
    "    collection_name=collection_name,\n",
    "    query_filter=models.Filter(\n",
    "        must=[\n",
    "            models.FieldCondition(\n",
    "                key=\"filename\", match=models.MatchValue(value=document_name)\n",
    "            )\n",
    "        ]\n",
    "    ),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query_vector_dense = self.dense_embeddings.get_dense_vector(query_text)\n",
    "# query_vector_sparse = self.dense_embeddings.get_sparse_vector(query_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([ScoredPoint(id=3, version=2, score=0.9999997, payload={'name': 'point_3'}, vector={'text-sparse': SparseVector(indices=[6, 7], values=[1.0, 2.0]), 'text-dense': [0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 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0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523, 0.025515523]}, shard_key=None, order_value=None)])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_query_dense_vector = [0.099] * 1536\n",
    "user_query_sparse_vector = {6: 2.0, 7: 3.0}\n",
    "\n",
    "\n",
    "# Dense search\n",
    "dense_results = client.search(\n",
    "    collection_name=collection_name,\n",
    "    query_vector=(\"text-dense\", user_query_dense_vector),\n",
    "    with_vectors=True,\n",
    ")\n",
    "\n",
    "# Sparse search\n",
    "sparse_results = client.search(\n",
    "    collection_name=collection_name,\n",
    "    query_vector=models.NamedSparseVector(\n",
    "        name=\"text-sparse\",\n",
    "        vector=models.SparseVector(\n",
    "            indices=list(user_query_sparse_vector.keys()),\n",
    "            values=list(user_query_sparse_vector.values()),\n",
    "        ),\n",
    "    ),\n",
    "    with_vectors=True,\n",
    ")\n",
    "\n",
    "dense_results, sparse_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rrf_fusion_separate_searches(dense_results, sparse_results, k=60):\n",
    "    combined_scores = {}\n",
    "\n",
    "    # Rank the dense results\n",
    "    for rank, point in enumerate(dense_results, start=1):\n",
    "        if point.id not in combined_scores:\n",
    "            combined_scores[point.id] = 0\n",
    "        combined_scores[point.id] += 1 / (k + rank)\n",
    "\n",
    "    # Rank the sparse results\n",
    "    for rank, point in enumerate(sparse_results, start=1):\n",
    "        if point.id not in combined_scores:\n",
    "            combined_scores[point.id] = 0\n",
    "        combined_scores[point.id] += 1 / (k + rank)\n",
    "\n",
    "    # Sort by the combined RRF score\n",
    "    fused_results = sorted(combined_scores.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    return fused_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point ID: 1, Combined RRF Score: 0.03278688524590164\n"
     ]
    }
   ],
   "source": [
    "# Apply RRF to combine the results\n",
    "fused_results = rrf_fusion_separate_searches(dense_results, sparse_results)\n",
    "\n",
    "# Output the fused results with combined RRF scores\n",
    "for point_id, score in fused_results:\n",
    "    print(f\"Point ID: {point_id}, Combined RRF Score: {score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 0.03278688524590164)]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fused_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[ScoredPoint(id=1, version=0, score=0.9999997, payload=None, vector=None, shard_key=None, order_value=None)],\n",
       " [ScoredPoint(id=1, version=0, score=5.0, payload=None, vector=None, shard_key=None, order_value=None)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hybrid search\n",
    "\n",
    "client.search_batch(\n",
    "    collection_name=collection_name,\n",
    "    requests=[\n",
    "        models.SearchRequest(\n",
    "            vector=models.NamedVector(\n",
    "                name=\"text-dense\",\n",
    "                vector=query_vector_dense,\n",
    "            ),\n",
    "            limit=10,\n",
    "        ),\n",
    "        models.SearchRequest(\n",
    "            vector=models.NamedSparseVector(\n",
    "                name=\"text-sparse\",\n",
    "                vector=models.SparseVector(\n",
    "                    indices=query_indices,\n",
    "                    values=query_values,\n",
    "                ),\n",
    "            ),\n",
    "            limit=10,\n",
    "        ),\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([6, 7])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_query_sparse_vector = {\n",
    "  6: 1.0,\n",
    "  7: 2.0\n",
    "}\n",
    "user_query_sparse_vector.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hybrid search with prefetch and RRF fusion\n",
    "\n",
    "user_query_dense_vector = [0.099] * 1536\n",
    "user_query_sparse_vector = {6: 2.0, 7: 3.0}\n",
    "\n",
    "sparse_dense_rrf_prefetch = models.Prefetch(\n",
    "    prefetch=[\n",
    "        models.Prefetch(\n",
    "            query=user_query_dense_vector,\n",
    "            using=\"text-dense\",\n",
    "            limit=25,\n",
    "        ),\n",
    "        models.Prefetch(\n",
    "            query=models.SparseVector(\n",
    "                indices=list(user_query_sparse_vector.keys()),\n",
    "                values=list(user_query_sparse_vector.values()),\n",
    "            ),\n",
    "            using=\"text-sparse\",\n",
    "            limit=25,\n",
    "        ),\n",
    "    ],\n",
    "    # RRF fusion\n",
    "    query=models.FusionQuery(\n",
    "        fusion=models.Fusion.RRF,\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = client.query_points(\n",
    "    collection_name=collection_name,\n",
    "    prefetch=[sparse_dense_rrf_prefetch],\n",
    "    query=user_query_dense_vector,\n",
    "    using=\"text-dense\", \n",
    "    with_payload=True,\n",
    "    with_vectors=True,\n",
    "    limit=10,  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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0.025515513, 0.025515513, 0.025515513, 0.025515513, 0.025515513], 'text-sparse': SparseVector(indices=[6, 7], values=[1.0, 2.0])}, shard_key=None, order_value=None)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'point_3'}, {'name': 'point_1'}, {'name': 'point_2'}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[{\"name\": point.payload[\"name\"]} for point in result.points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'point_1'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.points[0].payload['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_dense_rrf_prefetch = models.Prefetch(\n",
    "    prefetch=[\n",
    "        models.Prefetch(\n",
    "            prefetch=[\n",
    "                # First prefetch retrieves 100 documents with dense vectors\n",
    "                models.Prefetch(\n",
    "                    query=dense_vector_low_precision,\n",
    "                    using=\"dense-uint8\",  # Low precision dense vectors\n",
    "                    limit=100,\n",
    "                )\n",
    "            ],\n",
    "            # Then, rerank with high-precision dense vectors\n",
    "            query=user_query_dense_vector,\n",
    "            using=\"dense\",  # High precision dense vectors\n",
    "            limit=25,  # Rerank and reduce the number of documents\n",
    "        ),\n",
    "        # Another prefetch using sparse vectors\n",
    "        models.Prefetch(\n",
    "            query=models.SparseVector(\n",
    "                indices=list(sparse_vector.keys()),\n",
    "                values=list(sparse_vector.values()),\n",
    "            ),\n",
    "            using=\"sparse\",  # Sparse vector search\n",
    "            limit=25,\n",
    "        ),\n",
    "    ],\n",
    "    # RRF fusion combines the dense and sparse results\n",
    "    query=models.FusionQuery(\n",
    "        fusion=models.Fusion.RRF,  # Reciprocal Rank Fusion\n",
    "    ),\n",
    ")\n",
    "\n",
    "client.query_points(\n",
    "    collection_name=\"my-collection\",\n",
    "    prefetch=[sparse_dense_rrf_prefetch],\n",
    "    # Optional: You can use a late interaction model for final reranking\n",
    "    query=late_interaction_query_vectors,  # Optional reranking\n",
    "    using=\"late-interaction\",  # This model reranks the final results\n",
    "    with_payload=True,\n",
    "    limit=10,  # Final results\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query_vector_sparse = self.dense_embeddings.get_sparse_vector(query_text)\n",
    "\n",
    "from torch import cosine_similarity\n",
    "\n",
    "\n",
    "sparse_results = client.search(\n",
    "    collection_name=collection_name,\n",
    "    vector=None,\n",
    "    sparse_vector={\n",
    "        \"text-sparse\": {\n",
    "            \"indices\": list(user_query_sparse_vector.keys()),\n",
    "            \"values\": list(user_query_sparse_vector.values()),\n",
    "        }\n",
    "    },\n",
    "    limit=100,  # Retrieve more candidates to rerank later\n",
    "    with_payload=True,\n",
    ")\n",
    "\n",
    "\n",
    "# query_vector_dense = self.dense_embeddings.get_dense_vector(query_text)\n",
    "\n",
    "# Reranking based on dense vectors\n",
    "reranked_results = sorted(\n",
    "    sparse_results,\n",
    "    key=lambda point: cosine_similarity(point.vector[\"text-dense\"], query_vector_dense),\n",
    "    reverse=True  # Assuming higher cosine similarity is better\n",
    ")\n",
    "\n",
    "# Now limit to the top-N results after reranking\n",
    "top_reranked_results = reranked_results[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValidationError",
     "evalue": "1 validation error for PointStruct\nsparse_vector\n  Extra inputs are not permitted [type=extra_forbidden, input_value={'text-sparse': SparseVec... 7], values=[1.0, 2.0])}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.8/v/extra_forbidden",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m points \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m----> 2\u001b[0m     \u001b[43mPointStruct\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mid\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvector\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext-dense\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1536\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m        \u001b[49m\u001b[43msparse_vector\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext-sparse\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSparseVector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m7\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2.0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m        \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m ]\n\u001b[1;32m     11\u001b[0m client\u001b[38;5;241m.\u001b[39mupsert(\n\u001b[1;32m     12\u001b[0m     collection_name\u001b[38;5;241m=\u001b[39mcollection_name,\n\u001b[1;32m     13\u001b[0m     wait\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     14\u001b[0m     points\u001b[38;5;241m=\u001b[39mpoints,\n\u001b[1;32m     15\u001b[0m )\n",
      "File \u001b[0;32m~/DocuRAG/Api/venv/lib/python3.10/site-packages/pydantic/main.py:193\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m    192\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 193\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mself_instance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for PointStruct\nsparse_vector\n  Extra inputs are not permitted [type=extra_forbidden, input_value={'text-sparse': SparseVec... 7], values=[1.0, 2.0])}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.8/v/extra_forbidden"
     ]
    }
   ],
   "source": [
    "points = [\n",
    "    PointStruct(\n",
    "        id=str(1),\n",
    "        vector={\"text-dense\": [0.0] * 1536},\n",
    "        sparse_vector={\n",
    "            \"text-sparse\": models.SparseVector(indices=[6, 7], values=[1.0, 2.0])\n",
    "        },\n",
    "    )\n",
    "]\n",
    "\n",
    "client.upsert(\n",
    "    collection_name=collection_name,\n",
    "    wait=True,\n",
    "    points=points,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = [\n",
    "    PointStruct(\n",
    "        id=1,\n",
    "        vector={\"text-dense\": [0.1] * 1536},\n",
    "        payload={\"name\": f\"point_{1}\"},\n",
    "    ),\n",
    "    PointStruct(\n",
    "        id=1,\n",
    "        vector={\"text-sparse\": models.SparseVector(indices=[6, 7], values=[1.0, 2.0])},\n",
    "        payload={\"name\": f\"point_{1}\"},\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UpdateResult(operation_id=3, status=<UpdateStatus.COMPLETED: 'completed'>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.upsert(\n",
    "    collection_name=collection_name,\n",
    "    wait=True,\n",
    "    points=points,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[CollectionDescription(name='testfile')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections = client.get_collections().collections\n",
    "collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = [\n",
    "    PointStruct(\n",
    "        id=1,\n",
    "        vector=[0.1, 0.2, 0.3, 0.4],\n",
    "        payload={\n",
    "            \"document_id\": \"Doc_1\",\n",
    "            \"chunk_index\": 1,\n",
    "            \"filename\": \"Test_file\",\n",
    "            \"text\": \"This is a test file 2\",\n",
    "        },\n",
    "    ),\n",
    "    PointStruct(\n",
    "        id=2,\n",
    "        vector=[0.9, 0.9, 0.8, 0.9],\n",
    "        payload={\n",
    "            \"document_id\": \"Doc_1\",\n",
    "            \"chunk_index\": 2,\n",
    "            \"filename\": \"Test_file\",\n",
    "            \"text\": \"World\",\n",
    "        },\n",
    "    ),\n",
    "    PointStruct(\n",
    "        id=3,\n",
    "        vector=[0.9, 0.9, 0.8, 0.9],\n",
    "        payload={\n",
    "            \"document_id\": \"Doc_1\",\n",
    "            \"chunk_index\": 2,\n",
    "            \"filename\": \"Test_file\",\n",
    "            \"text\": \"Hello World\",\n",
    "        },\n",
    "    ),\n",
    "    PointStruct(\n",
    "        id=4,\n",
    "        vector=[0.9, 0.9, 0.8, 0.9],\n",
    "        payload={\n",
    "            \"document_id\": \"Doc_1\",\n",
    "            \"chunk_index\": 2,\n",
    "            \"filename\": \"Test_file\",\n",
    "            \"text\": \"Hello I'm Alex\",\n",
    "        },\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UpdateResult(operation_id=9, status=<UpdateStatus.COMPLETED: 'completed'>)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection_name = \"testfile\"\n",
    "client.upsert(\n",
    "    collection_name=collection_name,\n",
    "    wait=True,\n",
    "    points=points,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Record(id=1, payload={'document_id': 'Doc_1', 'chunk_index': 1, 'filename': 'Test_file', 'text': 'This is a test file 2'}, vector=None, shard_key=None, order_value=None),\n",
       " Record(id=2, payload={'document_id': 'Doc_1', 'chunk_index': 2, 'filename': 'Test_file', 'text': 'World'}, vector=None, shard_key=None, order_value=None),\n",
       " Record(id=3, payload={'document_id': 'Doc_1', 'chunk_index': 2, 'filename': 'Test_file', 'text': 'Hello World'}, vector=None, shard_key=None, order_value=None),\n",
       " Record(id=4, payload={'document_id': 'Doc_1', 'chunk_index': 2, 'filename': 'Test_file', 'text': \"Hello I'm Alex\"}, vector=None, shard_key=None, order_value=None)]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.retrieve(\n",
    "    collection_name=\"testfile\",\n",
    "    ids=[1, 2, 3, 4],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_vector = [0.1, 0.2, 0.3, 0.4]\n",
    "TEXT = \"test\"\n",
    "filter_conditions = models.Filter(\n",
    "            should=[\n",
    "                models.FieldCondition(\n",
    "                    key=\"text\",\n",
    "                    match=models.MatchText(text=\"World\"),\n",
    "                )\n",
    "            ]\n",
    "        )\n",
    "\n",
    "search_result = client.query_points(\n",
    "            collection_name=collection_name,\n",
    "            query=dense_vector,\n",
    "            query_filter=filter_conditions,\n",
    "            search_params=models.SearchParams(hnsw_ef=100, exact=False),\n",
    "            with_payload=[\"text\"],\n",
    "            limit=4,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QueryResponse(points=[ScoredPoint(id=3, version=9, score=0.87, payload={'text': 'Hello World'}, vector=None, shard_key=None, order_value=None), ScoredPoint(id=2, version=9, score=0.87, payload={'text': 'World'}, vector=None, shard_key=None, order_value=None)])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "for vec in search_result:\n",
    "    for scored_point in vec[1]:\n",
    "        print(scored_point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexabades/DocuRAG/Api/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/home/alexabades/DocuRAG/Api/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n",
      "/home/alexabades/DocuRAG/Api/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM\n",
    "from qdrant_client.http import models as qdrant_models\n",
    "\n",
    "# Load dense and sparse models\n",
    "dense_model_name = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
    "sparse_model_name = \"prithivida/Splade_PP_en_v1\"\n",
    "\n",
    "dense_tokenizer = AutoTokenizer.from_pretrained(dense_model_name)\n",
    "dense_model = AutoModel.from_pretrained(dense_model_name)\n",
    "\n",
    "sparse_tokenizer = AutoTokenizer.from_pretrained(sparse_model_name)\n",
    "sparse_model = AutoModelForMaskedLM.from_pretrained(sparse_model_name)\n",
    "\n",
    "# Example text chunk\n",
    "chunk = \"This is a sample text for testing dense and sparse vectors.\"\n"
   ]
  }
 ],
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