Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "from lightrag import QueryParam\n",
    "from src.config import Config\n",
    "from src.rag import RAGManager\n",
    "from symai.components import FileReader\n",
    "\n",
    "# If running in a Jupyter notebook, apply nest_asyncio\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()\n",
    "\n",
    "config = Config.load(\"config.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(level=logging.DEBUG)\n",
    "# Enable detailed logging for lightrag\n",
    "logger = logging.getLogger(\"lightrag\")\n",
    "logger.setLevel(logging.DEBUG)\n",
    "logger.disabled = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lightrag:Using the label default for PostgreSQL as identifier\n",
      "INFO:lightrag:Connected to PostgreSQL database at localhost:5432/rag\n",
      "INFO:lightrag:Checking if table LIGHTRAG_DOC_FULL exists...\n",
      "INFO:lightrag:Table LIGHTRAG_DOC_FULL exists\n",
      "INFO:lightrag:Checking if table LIGHTRAG_DOC_CHUNKS exists...\n",
      "INFO:lightrag:Table LIGHTRAG_DOC_CHUNKS exists\n",
      "INFO:lightrag:Checking if table LIGHTRAG_VDB_ENTITY exists...\n",
      "INFO:lightrag:Table LIGHTRAG_VDB_ENTITY exists\n",
      "INFO:lightrag:Checking if table LIGHTRAG_VDB_RELATION exists...\n",
      "INFO:lightrag:Table LIGHTRAG_VDB_RELATION exists\n",
      "INFO:lightrag:Checking if table LIGHTRAG_LLM_CACHE exists...\n",
      "INFO:lightrag:Table LIGHTRAG_LLM_CACHE exists\n",
      "INFO:lightrag:Checking if table LIGHTRAG_DOC_STATUS exists...\n",
      "INFO:lightrag:Table LIGHTRAG_DOC_STATUS exists\n",
      "INFO:lightrag:Checking if table LIGHTRAG_DOC_TAGS exists...\n",
      "INFO:lightrag:Table LIGHTRAG_DOC_TAGS exists\n",
      "INFO:lightrag:Finished checking all tables in PostgreSQL database\n",
      "INFO:lightrag:Logger initialized for working directory: rag_store\n",
      "DEBUG:lightrag:LightRAG init with param:\n",
      "  working_dir = rag_store,\n",
      "  embedding_cache_config = {'enabled': True, 'similarity_threshold': 0.85, 'use_llm_check': False},\n",
      "  kv_storage = PGKVStorage,\n",
      "  vector_storage = PGVectorStorage,\n",
      "  graph_storage = NoOpGraphStorage,\n",
      "  log_level = 0,\n",
      "  chunk_token_size = 1200,\n",
      "  chunk_overlap_token_size = 100,\n",
      "  tiktoken_model_name = gpt-4o-mini,\n",
      "  entity_extract_max_gleaning = 1,\n",
      "  entity_summary_to_max_tokens = 500,\n",
      "  node_embedding_algorithm = node2vec,\n",
      "  node2vec_params = {'dimensions': 1536, 'num_walks': 10, 'walk_length': 40, 'window_size': 2, 'iterations': 3, 'random_seed': 3},\n",
      "  embedding_func = {'embedding_dim': 1536, 'max_token_size': 8191, 'func': <function LightRAGBridge.embedding_func at 0x342acb1c0>},\n",
      "  embedding_batch_num = 8,\n",
      "  embedding_func_max_async = 16,\n",
      "  llm_model_func = <function LightRAGBridge.llm_model_func at 0x32d58d3f0>,\n",
      "  llm_model_name = meta-llama/Llama-3.2-1B-Instruct,\n",
      "  llm_model_max_token_size = 32768,\n",
      "  llm_model_max_async = 16,\n",
      "  llm_model_kwargs = {},\n",
      "  vector_db_storage_cls_kwargs = {},\n",
      "  enable_llm_cache = True,\n",
      "  enable_llm_cache_for_entity_extract = True,\n",
      "  addon_params = {'insert_batch_size': 20},\n",
      "  convert_response_to_json_func = <function convert_response_to_json at 0x1054fc5e0>,\n",
      "  doc_status_storage = PGDocStatusStorage,\n",
      "  chunking_func = <function chunking_by_token_size at 0x1055143a0>,\n",
      "  chunking_func_kwargs = {}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialize RAGManager with backend\n",
    "rag_manager = RAGManager(working_dir=config.local.working_dir)\n",
    "rag_manager = await RAGManager.init_with_backend(config=config)\n",
    "\n",
    "workspace = \"default\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:lightrag:Starting deletion for document doc-e90e20c0263668222902ae7938e93dc1\n",
      "DEBUG:lightrag:Found 78 chunks to delete\n",
      "DEBUG:lightrag:Chunk chunk-573eb10647e58df1d8a8b5c44e099193 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-573eb10647e58df1d8a8b5c44e099193 has 0 related relations\n",
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      "DEBUG:lightrag:Chunk chunk-94bd3a6b447221c59e905dcd15754c82 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-c3bac090be40f6c59490e7eee5f4f251 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-c3bac090be40f6c59490e7eee5f4f251 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-1c516590db22ca61623444b9ad2d9fa3 has 0 related entities\n",
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      "DEBUG:lightrag:Chunk chunk-a3522319b189c653bd5b204911d43426 has 0 related entities\n",
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      "DEBUG:lightrag:Chunk chunk-b98f0637355a968ab8069f81dd16fad7 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-b98f0637355a968ab8069f81dd16fad7 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-1d371c5e20732febd35c88423bbf9787 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-1d371c5e20732febd35c88423bbf9787 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-7c961f4986e63c4a1f9aa648635fb4e0 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-7c961f4986e63c4a1f9aa648635fb4e0 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-3e8bc331694c6220af5cf4d0129b88a0 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-3e8bc331694c6220af5cf4d0129b88a0 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-1784e03ec06286c26da0af7e45438a28 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-1784e03ec06286c26da0af7e45438a28 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-301681aba11fff304f0ed92e431bd502 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-301681aba11fff304f0ed92e431bd502 has 0 related relations\n",
      "INFO:lightrag:Successfully deleted 78 vectors from chunks\n",
      "INFO:lightrag:Successfully deleted 78 records from text_chunks\n",
      "INFO:lightrag:Successfully deleted 1 records from full_docs\n",
      "INFO:lightrag:Successfully deleted 1 records from doc_status\n",
      "INFO:lightrag:full doc and chunk data had been saved into postgresql db!\n",
      "INFO:lightrag:full doc and chunk data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "INFO:lightrag:Successfully deleted document doc-e90e20c0263668222902ae7938e93dc1 and related data. Deleted 0 entities and 0 relationships. Updated 0 entities and 0 relationships.\n",
      "DEBUG:lightrag:Starting deletion for document doc-e62c26c7fd5ee70f14500211903ef175\n",
      "DEBUG:lightrag:Found 78 chunks to delete\n",
      "DEBUG:lightrag:Chunk chunk-139a1761ce4c39d92ff73d908a1946bd has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-139a1761ce4c39d92ff73d908a1946bd has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-fe19daf7cde055518e8488ca26ad3175 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-fe19daf7cde055518e8488ca26ad3175 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-1109c8f5688da3fbfe0800f042a582b5 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-1109c8f5688da3fbfe0800f042a582b5 has 0 related relations\n",
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      "DEBUG:lightrag:Chunk chunk-f461a553bc965be323bf6dba102cf349 has 0 related relations\n",
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      "DEBUG:lightrag:Chunk chunk-4d0ab8718902b8335dfc9da11b5a42cf has 0 related relations\n",
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      "DEBUG:lightrag:Chunk chunk-b37dc98474ce125a12203b3c5de3189c has 0 related relations\n",
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      "DEBUG:lightrag:Chunk chunk-5b615dd500802811de9dff0ef967ec16 has 0 related relations\n",
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      "DEBUG:lightrag:Chunk chunk-1a17f92d59d4edb07def33b2accce53b has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-81e90b41f5be622e5e9990e22ed005d8 has 0 related entities\n",
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      "DEBUG:lightrag:Chunk chunk-d8e0fa5614c6c50871eb9285c1a35310 has 0 related relations\n",
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      "DEBUG:lightrag:Chunk chunk-03c1ce2d51221dc83482281b1261d645 has 0 related relations\n",
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      "DEBUG:lightrag:Chunk chunk-6eff85b48e1279ad582034d83975046d has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-b0b14a2d4fe381d0f5aba571b65a898b has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-b0b14a2d4fe381d0f5aba571b65a898b has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-6523e4643ef438621d3e22de72b6a3bf has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-6523e4643ef438621d3e22de72b6a3bf has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-7552e35897e2723959dc6296855de7bc has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-7552e35897e2723959dc6296855de7bc has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-1d7d1b94a054961149f9dc68609c4de3 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-1d7d1b94a054961149f9dc68609c4de3 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-5db02e5bf9c2030fe914237f23c2b8fb has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-5db02e5bf9c2030fe914237f23c2b8fb has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-125dd3e612b6cf2ce2c54e67c48ad833 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-125dd3e612b6cf2ce2c54e67c48ad833 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-a7c2225092518705f704424bcc1feed9 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-a7c2225092518705f704424bcc1feed9 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-bb8aab9327fdc479e0d708cdf05848e5 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-bb8aab9327fdc479e0d708cdf05848e5 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-72032d8aea2d518ebcf857d350a26336 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-72032d8aea2d518ebcf857d350a26336 has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-24f3dc7b0e16c9155b07bb34f559851f has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-24f3dc7b0e16c9155b07bb34f559851f has 0 related relations\n",
      "DEBUG:lightrag:Chunk chunk-79b36655f932328a47d0ebd5eed0aae8 has 0 related entities\n",
      "DEBUG:lightrag:Chunk chunk-79b36655f932328a47d0ebd5eed0aae8 has 0 related relations\n",
      "INFO:lightrag:Successfully deleted 78 vectors from chunks\n",
      "INFO:lightrag:Successfully deleted 78 records from text_chunks\n",
      "INFO:lightrag:Successfully deleted 1 records from full_docs\n",
      "INFO:lightrag:Successfully deleted 1 records from doc_status\n",
      "INFO:lightrag:full doc and chunk data had been saved into postgresql db!\n",
      "INFO:lightrag:full doc and chunk data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "INFO:lightrag:Successfully deleted document doc-e62c26c7fd5ee70f14500211903ef175 and related data. Deleted 0 entities and 0 relationships. Updated 0 entities and 0 relationships.\n"
     ]
    }
   ],
   "source": [
    "# Clean env first\n",
    "await rag_manager.clear_workspace(workspace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache entries after clearing: {}\n"
     ]
    }
   ],
   "source": [
    "# Clear the LLM response cache\n",
    "sql = \"DELETE FROM LIGHTRAG_LLM_CACHE WHERE workspace = $1\"\n",
    "await rag_manager.rag_manager.llm_response_cache.db.execute(sql, {\"workspace\": workspace})\n",
    "\n",
    "# Verify it's empty\n",
    "cache_data = await rag_manager.rag_manager.llm_response_cache.get_all()\n",
    "print(\"Cache entries after clearing:\", cache_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "assert not await rag_manager.list(workspace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(await rag_manager.list(workspace))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "D1 = \"/Users/ryang/Work/ExtensityAI/RAG/testfiles/Align-RUDDER.pdf\"\n",
    "D2 = \"/Users/ryang/Work/ExtensityAI/RAG/testfiles/symbolicai_no_refs.pdf\"\n",
    "\n",
    "reader = FileReader()\n",
    "\n",
    "get_id = lambda s: s.split(\"/\")[-1].split(\".\")[0].replace(\" \", \"_\").lower()\n",
    "\n",
    "doc_contents = [\n",
    "    reader(D1).value[0],\n",
    "    reader(D2).value[0]\n",
    "]\n",
    "doc_ids = [\n",
    "    get_id(D1),\n",
    "    get_id(D2)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:lightrag:No documents found with name: align-rudder\n",
      "2025-03-12 11:52:15,521 No documents found with name: align-rudder\n",
      "INFO:lightrag:[New Docs] inserting 1 docs\n",
      "2025-03-12 11:52:16,322 [New Docs] inserting 1 docs\n",
      "INFO:lightrag:[New Chunks] inserting 78 chunks\n",
      "2025-03-12 11:52:16,327 [New Chunks] inserting 78 chunks\n",
      "INFO:lightrag:Inserting 78 vectors to chunks\n",
      "2025-03-12 11:52:16,327 Inserting 78 vectors to chunks\n",
      "Generating embeddings: 100%|██████████| 10/10 [00:02<00:00,  3.74batch/s]\n",
      "INFO:lightrag:full doc and chunk data had been saved into postgresql db!\n",
      "2025-03-12 11:52:19,028 full doc and chunk data had been saved into postgresql db!\n",
      "INFO:lightrag:full doc and chunk data had been saved into postgresql db!\n",
      "2025-03-12 11:52:19,028 full doc and chunk data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "2025-03-12 11:52:19,029 vector data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "2025-03-12 11:52:19,030 vector data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "2025-03-12 11:52:19,030 vector data had been saved into postgresql db!\n",
      "\u001b[32m2025-03-12 11:52:19.032\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msrc.rag\u001b[0m:\u001b[36mchunk_and_upsert\u001b[0m:\u001b[36m231\u001b[0m - \u001b[1mSuccessfully inserted 1 documents with their chunks\u001b[0m\n",
      "WARNING:lightrag:No documents found with name: symbolicai_no_refs\n",
      "2025-03-12 11:52:19,033 No documents found with name: symbolicai_no_refs\n",
      "INFO:lightrag:[New Docs] inserting 1 docs\n",
      "2025-03-12 11:52:19,957 [New Docs] inserting 1 docs\n",
      "INFO:lightrag:[New Chunks] inserting 78 chunks\n",
      "2025-03-12 11:52:19,962 [New Chunks] inserting 78 chunks\n",
      "INFO:lightrag:Inserting 78 vectors to chunks\n",
      "2025-03-12 11:52:19,963 Inserting 78 vectors to chunks\n",
      "Generating embeddings: 100%|██████████| 10/10 [00:02<00:00,  4.55batch/s]\n",
      "INFO:lightrag:full doc and chunk data had been saved into postgresql db!\n",
      "2025-03-12 11:52:22,169 full doc and chunk data had been saved into postgresql db!\n",
      "INFO:lightrag:full doc and chunk data had been saved into postgresql db!\n",
      "2025-03-12 11:52:22,170 full doc and chunk data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "2025-03-12 11:52:22,170 vector data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "2025-03-12 11:52:22,171 vector data had been saved into postgresql db!\n",
      "INFO:lightrag:vector data had been saved into postgresql db!\n",
      "2025-03-12 11:52:22,171 vector data had been saved into postgresql db!\n",
      "\u001b[32m2025-03-12 11:52:22.172\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msrc.rag\u001b[0m:\u001b[36mchunk_and_upsert\u001b[0m:\u001b[36m231\u001b[0m - \u001b[1mSuccessfully inserted 1 documents with their chunks\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Replace content in doc_id\n",
    "await rag_manager.chunk_and_upsert(document_contents=doc_contents[0], document_ids=doc_ids[0], workspace=workspace)\n",
    "await rag_manager.chunk_and_upsert(document_contents=doc_contents[0], document_ids=doc_ids[1], workspace=workspace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "workspace = \"default\"\n",
    "\n",
    "query_params = QueryParam(\n",
    "    mode=\"naive\",\n",
    "    top_k=5,\n",
    "    only_need_context=True,\n",
    "    return_doc_names=True,\n",
    "    response_type=\"Long answer with multiple sections and paragraphs; list facts and details.\"\n",
    ")\n",
    "query = \"artificial intelligence\"\n",
    "response = await rag_manager.query(query, param=query_params, workspace=workspace, tags=['tag1', 'tag2'])\n",
    "for chunk in response['response']:\n",
    "    print(chunk)\n",
    "    print(\"----------------------------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Check what's in the cache\n",
    "cache_data = await rag_manager.rag_manager.llm_response_cache.get_all()\n",
    "print(\"\\nAll cache entries:\")\n",
    "for key, value in cache_data.items():\n",
    "    print(f\"\\nKey: {key}\")\n",
    "    for hash_key, hash_data in value.items():\n",
    "        print(f\"\\nHash Key: {hash_key}\")\n",
    "        print(\"Cache Data:\")\n",
    "        # Print all available fields\n",
    "        for field, content in hash_data.items():\n",
    "            print(f\"  - {field}: {content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await rag_manager.get_document_tags('symbolicai_no_refs', workspace=\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await rag_manager.get_document_tags('align-rudder', workspace=\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await rag_manager.assign_tag('symbolicai_no_refs', 'symai')\n",
    "await rag_manager.assign_tag('align-rudder', 'other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await rag_manager.remove_tag('align-rudder', 'other')\n",
    "await rag_manager.get_document_tags('align-rudder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await rag_manager.get_available_tags()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await rag_manager.delete_by_tag(['other'])"
   ]
  }
 ],
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