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    {
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     "text": [
      "/usr/local/lib/python3.12/site-packages/pymongo/synchronous/collection.py:1920: UserWarning: use an explicit session with no_cursor_timeout=True otherwise the cursor may still timeout after 30 minutes, for more info see https://mongodb.com/docs/v4.4/reference/method/cursor.noCursorTimeout/#session-idle-timeout-overrides-nocursortimeout\n",
      "  return Cursor(self, *args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping document loop\n",
      "Stopping document loop\n"
     ]
    }
   ],
   "source": [
    "# See README for more info on how the FeaturePipeline works\n",
    "# The Ingestion pipeline is part of the FeaturePipeline\n",
    "# Make sure to ollama serve before running!\n",
    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
    "from qdrant_client.http.models import Distance, VectorParams, PointStruct\n",
    "from shared import getMongoClient, getQdrantClient, getEmbeddingsModel\n",
    "\n",
    "# Create a mongoDB connection\n",
    "mongoHost = getMongoClient()\n",
    "\n",
    "# Create a qdrant connection\n",
    "qClient = getQdrantClient()\n",
    "\n",
    "# Create qdrant collections to store embeddings\n",
    "if not qClient.collection_exists(\"Github\"):\n",
    "    qClient.create_collection(\n",
    "        collection_name=\"Github\",\n",
    "        vectors_config=VectorParams(size=3072, distance=Distance.COSINE),\n",
    "    )\n",
    "if not qClient.collection_exists(\"Document\"):\n",
    "    qClient.create_collection(\n",
    "        collection_name=\"Document\",\n",
    "        vectors_config=VectorParams(size=3072, distance=Distance.COSINE),\n",
    "    )\n",
    "\n",
    "# Ingestion Pipeline Setup\n",
    "# Define a text cleaner\n",
    "def cleanText(text):\n",
    "    return ''.join(char for char in text if 32 <= ord(char) <= 126)\n",
    "\n",
    "# Setup the text chunker\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=500,\n",
    "    chunk_overlap=20,\n",
    "    length_function=len,\n",
    "    is_separator_regex=False,\n",
    ")\n",
    "\n",
    "# Setup the text embedder\n",
    "embeddingsModel = getEmbeddingsModel()\n",
    "\n",
    "# Running the ingestion pipeline\n",
    "# Store all documents from each MongoDB collection into qdrant\n",
    "mongoDatabase =  mongoHost[\"twin\"]\n",
    "collections = mongoDatabase.list_collection_names()\n",
    "for collection in collections:\n",
    "    mongoCollection = mongoDatabase[collection]\n",
    "\n",
    "    documents = mongoCollection.find(no_cursor_timeout=True)\n",
    "    id = 0\n",
    "    try:\n",
    "        for document in documents:\n",
    "            # For each document, split it into chunks\n",
    "            link = document[\"link\"]\n",
    "            resultType = document[\"type\"]\n",
    "            text = document[\"content\"]\n",
    "            text = cleanText(text)\n",
    "            chunks = text_splitter.split_text(text)\n",
    "            chunkNum = 0\n",
    "            embeddings = embeddingsModel.embed_documents(chunks)\n",
    "            for chunk in chunks:\n",
    "                # Create embeddings for each chunk, of length 3072 using the embedding model\n",
    "                # Store the embedding along with some metadata into the Qdrant vector database\n",
    "                qClient.upsert(collection_name=resultType, wait=True, points=[PointStruct(id=id, vector=embeddings[chunkNum], payload={\"link\": link, \"type\": resultType, \"chunk\": chunkNum, \"text\": chunk})])\n",
    "                chunkNum += 1\n",
    "                id += 1\n",
    "    except:\n",
    "        print(\"Stopping document loop\")\n",
    "    \n"
   ]
  }
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