{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe1cf4b8-4fee-49d9-aad5-18adabaca692",
   "metadata": {},
   "source": [
    "# SemaDB\n",
    "\n",
    "> [SemaDB](https://www.semafind.com/products/semadb) from [SemaFind](https://www.semafind.com) is a no fuss vector similarity database for building AI applications. The hosted `SemaDB Cloud` offers a no fuss developer experience to get started.\n",
    "\n",
    "The full documentation of the API along with examples and an interactive playground is available on [RapidAPI](https://rapidapi.com/semafind-semadb/api/semadb).\n",
    "\n",
    "This notebook demonstrates usage of the `SemaDB Cloud` vector store."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8c1970-52f0-4834-8f06-3ca8f7fac857",
   "metadata": {},
   "source": [
    "## Load document embeddings\n",
    "\n",
    "To run things locally, we are using [Sentence Transformers](https://www.sbert.net/) which are commonly used for embedding sentences. You can use any embedding model LangChain offers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "386a6b49-edee-45f2-9c0e-ebc125507ece",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bd07a44-34fd-4318-8033-4c8dbd327559",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "\n",
    "embeddings = HuggingFaceEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0079bdf-b3cd-4856-85d5-f7787f5d93d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114\n"
     ]
    }
   ],
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "\n",
    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
    "documents = loader.load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0)\n",
    "docs = text_splitter.split_documents(documents)\n",
    "print(len(docs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ed5523-330d-4697-9008-c910044ac45a",
   "metadata": {},
   "source": [
    "## Connect to SemaDB\n",
    "\n",
    "SemaDB Cloud uses [RapidAPI keys](https://rapidapi.com/semafind-semadb/api/semadb) to authenticate. You can obtain yours by creating a free RapidAPI account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4ffeeef-e6f5-4bcc-8c97-0e4222ca8282",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "SemaDB API Key: ········\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"SEMADB_API_KEY\"] = getpass.getpass(\"SemaDB API Key:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba5f7a81-0f59-448a-93a8-5d8bf3bfc0f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import SemaDB\n",
    "from langchain.vectorstores.utils import DistanceStrategy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320f743c-39ae-456c-8c20-0683196358a4",
   "metadata": {},
   "source": [
    "The parameters to the SemaDB vector store reflect the API directly:\n",
    "\n",
    "- \"mycollection\": is the collection name in which we will store these vectors.\n",
    "- 768: is dimensions of the vectors. In our case, the sentence transformer embeddings yield 768 dimensional vectors.\n",
    "- API_KEY: is your RapidAPI key.\n",
    "- embeddings: correspond to how the embeddings of documents, texts and queries will be generated.\n",
    "- DistanceStrategy: is the distance metric used. The wrapper automatically normalises vectors if COSINE is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1cb1f78-c25e-41a7-8001-6c84d51514ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db = SemaDB(\"mycollection\", 768, embeddings, DistanceStrategy.COSINE)\n",
    "\n",
    "# Create collection if running for the first time. If the collection\n",
    "# already exists this will fail.\n",
    "db.create_collection()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44348469-1d1f-4f3e-9af3-a955aec3dd71",
   "metadata": {},
   "source": [
    "The SemaDB vector store wrapper adds the document text as point metadata to collect later. Storing large chunks of text is *not recommended*. If you are indexing a large collection, we instead recommend storing references to the documents such as external Ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9adca5d3-e534-4fd2-aace-f436de4630ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['813c7ef3-9797-466b-8afa-587115592c6c',\n",
       " 'fc392f7f-082b-4932-bfcc-06800db5e017']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.add_documents(docs)[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb177b0d-148b-4cbc-86cc-b62dff135a9d",
   "metadata": {},
   "source": [
    "## Similarity Search\n",
    "\n",
    "We use the default LangChain similarity search interface to search for the most similar sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7536aba2-a757-4a3f-beda-79cfee5c34cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
     ]
    }
   ],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = db.similarity_search(query)\n",
    "print(docs[0].page_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a51e940e-487e-484d-9dc4-1aa1a6371660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Document(page_content='And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'text': 'And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.'}),\n",
       " 0.42369342)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs = db.similarity_search_with_score(query)\n",
    "docs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79aec3f4-d4d8-4c51-b4b2-074b6c22c3c0",
   "metadata": {},
   "source": [
    "## Clean up\n",
    "\n",
    "You can delete the collection to remove all data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b00afad5-8ec1-4c19-be6b-1c2ae2d5fead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.delete_collection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "239a0bca-5c88-401f-9828-1cb0b652e7d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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