how to connect and reconnect to a weaviate vectorstore in langchain

langchain-demo.ipynb

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      "Started /home/vscode/.cache/weaviate-embedded: process ID 4863\n"
     ]
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     "text": [
      "{\"action\":\"startup\",\"default_vectorizer_module\":\"none\",\"level\":\"info\",\"msg\":\"the default vectorizer modules is set to \\\"none\\\", as a result all new schema classes without an explicit vectorizer setting, will use this vectorizer\",\"time\":\"2023-06-15T11:46:08Z\"}\n",
      "{\"action\":\"startup\",\"auto_schema_enabled\":true,\"level\":\"info\",\"msg\":\"auto schema enabled setting is set to \\\"true\\\"\",\"time\":\"2023-06-15T11:46:08Z\"}\n",
      "{\"action\":\"hnsw_vector_cache_prefill\",\"count\":3000,\"index_id\":\"karpathy_gpt_ZT24y9VwtKZf\",\"level\":\"info\",\"limit\":1000000000000,\"msg\":\"prefilled vector cache\",\"time\":\"2023-06-15T11:46:08Z\",\"took\":193942}\n",
      "{\"level\":\"warning\",\"msg\":\"Multiple vector spaces are present, GraphQL Explore and REST API list objects endpoint module include params has been disabled as a result.\",\"time\":\"2023-06-15T11:46:08Z\"}\n",
      "{\"action\":\"grpc_startup\",\"level\":\"info\",\"msg\":\"grpc server listening at [::]:50051\",\"time\":\"2023-06-15T11:46:08Z\"}\n",
      "{\"action\":\"restapi_management\",\"level\":\"info\",\"msg\":\"Serving weaviate at http://127.0.0.1:6666\",\"time\":\"2023-06-15T11:46:08Z\"}\n",
      "/usr/local/lib/python3.10/subprocess.py:1072: ResourceWarning: subprocess 4863 is still running\n",
      "  _warn(\"subprocess %s is still running\" % self.pid,\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    }
   ],
   "source": [
    "import weaviate\n",
    "\n",
    "client = weaviate.Client(embedded_options=weaviate.embedded.EmbeddedOptions())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "splits = [\n",
    "    \"Artificial Intelligence (AI) is revolutionizing the way we interact with technology in our daily lives.\",\n",
    "    \"From autonomous driving to personalized recommendations on streaming platforms, AI applications are becoming ubiquitous.\",\n",
    "    \"With advances in machine learning and deep learning, AI systems are becoming increasingly capable and intelligent.\",\n",
    "    \"However, the rise of AI also raises important ethical questions about data privacy and algorithmic bias.\",\n",
    "    \"As AI continues to evolve, it's essential to prioritize transparency and fairness in the design and implementation of AI systems.\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Weaviate\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "\n",
    "index_name = \"Karpathy_gpt\"\n",
    "embeddings = OpenAIEmbeddings()\n",
    "vectorstore_new = Weaviate.from_texts(\n",
    "    splits, embeddings, client=client, index_name=index_name, text_key=\"text\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content=\"As AI continues to evolve, it's essential to prioritize transparency and fairness in the design and implementation of AI systems.\", metadata={})]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"What to prioritise?\"\n",
    "matched_docs = vectorstore_new.similarity_search(query, k=1)\n",
    "matched_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorstore_weviate = Weaviate(\n",
    "    client=client,\n",
    "    index_name=index_name,\n",
    "    text_key=\"text\",\n",
    "    by_text=False,\n",
    "    embedding=embeddings,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content=\"As AI continues to evolve, it's essential to prioritize transparency and fairness in the design and implementation of AI systems.\", metadata={})]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"What to prioritise?\"\n",
    "matched_docs = vectorstore_weviate.similarity_search(query, k=1)\n",
    "matched_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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