{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce0f17b9",
   "metadata": {},
   "source": [
    "# Weaviate Hybrid Search\n",
    "\n",
    ">[Weaviate](https://weaviate.io/developers/weaviate) is an open source vector database.\n",
    "\n",
    ">[Hybrid search](https://weaviate.io/blog/hybrid-search-explained) is a technique that combines multiple search algorithms to improve the accuracy and relevance of search results. It uses the best features of both keyword-based search algorithms with vector search techniques.\n",
    "\n",
    ">The `Hybrid search in Weaviate` uses sparse and dense vectors to represent the meaning and context of search queries and documents.\n",
    "\n",
    "This notebook shows how to use `Weaviate hybrid search` as a LangChain retriever."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bba863a2-977c-4add-b5f4-bfc33a80eae5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install weaviate-client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c10dd962",
   "metadata": {},
   "outputs": [],
   "source": [
    "import weaviate\n",
    "import os\n",
    "\n",
    "WEAVIATE_URL = \"...\"\n",
    "client = weaviate.Client(\n",
    "    url=WEAVIATE_URL,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f47a2bfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever\n",
    "from langchain.schema import Document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2eff08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = WeaviateHybridSearchRetriever(client, index_name=\"LangChain\", text_key=\"text\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd8a7b17",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = [Document(page_content=\"foo\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c5970db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3f79d151-fb84-44cf-85e0-8682bfe145e0']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.add_documents(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf7dbb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='foo', metadata={})]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.get_relevant_documents(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2bc87c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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