notebooks/01_demo.ipynb

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   "source": [
    "# Introduction"
   ]
  },
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   "source": [
    "This notebook shows how to work with the Streamlit Weaviate connection. \n",
    "\n",
    "The Weaviate Streamlit connection is a Python package that allows you to easily create Streamlit apps that loads data from a Weaviate instance."
   ]
  },
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   "source": [
    "# Setup\n",
    "\n",
    "Imports & environment variables"
   ]
  },
  {
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   "source": [
    "from st_weaviate_connection import WeaviateConnection\n",
    "import streamlit as st\n",
    "import os\n",
    "\n",
    "weaviate_url = os.environ[\"WEAVIATE_URL\"]\n",
    "weaviate_apikey = os.environ[\"WEAVIATE_API_KEY\"]\n",
    "cohere_apikey = os.environ[\"COHERE_API_KEY\"]  # Optional (for semantic search)"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "# Usage"
   ]
  },
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   "source": [
    "## Requirements"
   ]
  },
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     "slide_type": "fragment"
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   "source": [
    "#### Weaviate Cloud instance\n",
    "\n",
    "The easiest way to use this package is:\n",
    "- With a [Weaviate Cloud](https://console.weaviate.cloud) instance\n",
    "- Using the URL and the API key."
   ]
  },
  {
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   "source": [
    "This demo uses the following read-only credentials:\n",
    "\n",
    "```python\n",
    "weaviate_url = \"https://hha2nvjsruetknc5vxwrwa.c0.europe-west2.gcp.weaviate.cloud\"\n",
    "weaviate_apikey = \"nMZuw1z1zVtnjkXXOMGx9Ows7YWGsakItdus\"\n",
    "```"
   ]
  },
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   "metadata": {
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     "slide_type": "skip"
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   "source": [
    "**Note**: You can create a free Weaviate Cloud instance [here](https://console.weaviate.cloud)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "#### (Optional) Inference API key\n",
    "\n",
    "The demo notebook uses hybrid search, which combines semantic search with a traditional (keyword) search to provide more accurate results. \n",
    "\n",
    "The `Movie` collection is set up to use Cohere for semantic search. \n",
    "\n",
    "- If you do not have an Cohere API key, you can sign up on their website.\n",
    "- If you do not wish to use the semantic search part of hybrid search, set the `alpha` value to `0` in the `query` function."
   ]
  },
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   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Connect to a Weaviate Cloud instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "slideshow": {
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   "outputs": [
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     "text": [
      "2024-07-31 18:50:58.220 \n",
      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
      "  command:\n",
      "\n",
      "    streamlit run /Users/jphwang/Library/Caches/pypoetry/virtualenvs/st-weaviate-connection-6uLZqwja-py3.11/lib/python3.11/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
     ]
    }
   ],
   "source": [
    "conn = st.connection(\n",
    "    \"weaviate\",\n",
    "    type=WeaviateConnection,\n",
    "    url=weaviate_url,\n",
    "    api_key=weaviate_apikey,\n",
    "    additional_headers={\"X-Cohere-Api-Key\": cohere_apikey},\n",
    ")"
   ]
  },
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   "source": [
    "## Querying Data"
   ]
  },
  {
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   "source": [
    "There are two convenience methods in the package to query data:\n",
    "\n",
    "- `.query()`: Perform a hybrid search, which is a weighted combination of a semantic search and a keyword search.\n",
    "- `.graphql_query()`: Perform a raw GraphQL query"
   ]
  },
  {
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   "source": [
    "### Basic hybrid search"
   ]
  },
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   "metadata": {
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   "source": [
    "A hybrid search is a weighted combination of a semantic search and a keyword search.\n",
    "\n",
    "For basic hybrid search, just provide the collection name and the search term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>budget</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>revenue</th>\n",
       "      <th>overview</th>\n",
       "      <th>release_year</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83542</td>\n",
       "      <td>6786</td>\n",
       "      <td>102000000</td>\n",
       "      <td>Everything is Connected</td>\n",
       "      <td>Cloud Atlas</td>\n",
       "      <td>130482868</td>\n",
       "      <td>A set of six nested stories spanning time betw...</td>\n",
       "      <td>2012</td>\n",
       "      <td>[Drama, Science Fiction]</td>\n",
       "      <td>6.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1124</td>\n",
       "      <td>15086</td>\n",
       "      <td>40000000</td>\n",
       "      <td>Are You Watching Closely?</td>\n",
       "      <td>The Prestige</td>\n",
       "      <td>109676311</td>\n",
       "      <td>A mysterious story of two magicians whose inte...</td>\n",
       "      <td>2006</td>\n",
       "      <td>[Drama, Mystery, Science Fiction]</td>\n",
       "      <td>8.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>419704</td>\n",
       "      <td>6271</td>\n",
       "      <td>87500000</td>\n",
       "      <td>The answers we seek are just outside our reach</td>\n",
       "      <td>Ad Astra</td>\n",
       "      <td>127461872</td>\n",
       "      <td>The near future, a time when both hope and har...</td>\n",
       "      <td>2019</td>\n",
       "      <td>[Science Fiction, Drama]</td>\n",
       "      <td>6.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9294</td>\n",
       "      <td>1118</td>\n",
       "      <td>32000000</td>\n",
       "      <td>Some things in life just can't be explained.</td>\n",
       "      <td>Phenomenon</td>\n",
       "      <td>152000000</td>\n",
       "      <td>An ordinary man sees a bright light descend fr...</td>\n",
       "      <td>1996</td>\n",
       "      <td>[Drama, Romance, Science Fiction, Fantasy]</td>\n",
       "      <td>6.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58244</td>\n",
       "      <td>2431</td>\n",
       "      <td>60000000</td>\n",
       "      <td>Two worlds. One future.</td>\n",
       "      <td>Upside Down</td>\n",
       "      <td>22187813</td>\n",
       "      <td>In an alternate universe where twinned worlds ...</td>\n",
       "      <td>2012</td>\n",
       "      <td>[Romance, Science Fiction, Drama, Fantasy]</td>\n",
       "      <td>6.277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   movie_id  vote_count     budget  \\\n",
       "0     83542        6786  102000000   \n",
       "1      1124       15086   40000000   \n",
       "2    419704        6271   87500000   \n",
       "3      9294        1118   32000000   \n",
       "4     58244        2431   60000000   \n",
       "\n",
       "                                          tagline         title    revenue  \\\n",
       "0                         Everything is Connected   Cloud Atlas  130482868   \n",
       "1                       Are You Watching Closely?  The Prestige  109676311   \n",
       "2  The answers we seek are just outside our reach      Ad Astra  127461872   \n",
       "3    Some things in life just can't be explained.    Phenomenon  152000000   \n",
       "4                         Two worlds. One future.   Upside Down   22187813   \n",
       "\n",
       "                                            overview  release_year  \\\n",
       "0  A set of six nested stories spanning time betw...          2012   \n",
       "1  A mysterious story of two magicians whose inte...          2006   \n",
       "2  The near future, a time when both hope and har...          2019   \n",
       "3  An ordinary man sees a bright light descend fr...          1996   \n",
       "4  In an alternate universe where twinned worlds ...          2012   \n",
       "\n",
       "                                       genres  vote_average  \n",
       "0                    [Drama, Science Fiction]         6.882  \n",
       "1           [Drama, Mystery, Science Fiction]         8.203  \n",
       "2                    [Science Fiction, Drama]         6.100  \n",
       "3  [Drama, Romance, Science Fiction, Fantasy]         6.402  \n",
       "4  [Romance, Science Fiction, Drama, Fantasy]         6.277  "
      ]
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = conn.query(\n",
    "    collection_name=\"MovieDemo\",\n",
    "    query=\"Fantasy or sci-fi drama\",\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
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     "slide_type": "subslide"
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   "source": [
    "Additional parameters can be provided for more precise search results. Review the docstring for more information."
   ]
  },
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       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
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       "\u001b[0;34m\u001b[0m        \u001b[0mtarget_vectors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTargetVectorJoinType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mquery_properties\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mreturn_properties\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0malpha\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"\u001b[0m\n",
       "\u001b[0;34m        Query a Weaviate collection using a simplified hybrid query.\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m        Parameters\u001b[0m\n",
       "\u001b[0;34m        ----------\u001b[0m\n",
       "\u001b[0;34m        collection_name : str\u001b[0m\n",
       "\u001b[0;34m            The name of the collection to query.\u001b[0m\n",
       "\u001b[0;34m        query : str\u001b[0m\n",
       "\u001b[0;34m            The query to search for.\u001b[0m\n",
       "\u001b[0;34m        limit : int, optional\u001b[0m\n",
       "\u001b[0;34m            The number of results to return.\u001b[0m\n",
       "\u001b[0;34m            Default: 10.\u001b[0m\n",
       "\u001b[0;34m        filters : Filter, optional\u001b[0m\n",
       "\u001b[0;34m            The filters to apply to the query.\u001b[0m\n",
       "\u001b[0;34m            Import the `WeaviateFilter` class from `st_weaviate_connection` to use this argument.\u001b[0m\n",
       "\u001b[0;34m        target_vectors : string, List[string], List[TargetVectors], optional\u001b[0m\n",
       "\u001b[0;34m            The target vector(s) to search in the semantic search part of the query.\u001b[0m\n",
       "\u001b[0;34m            Only required if the target collection uses named vectors.\u001b[0m\n",
       "\u001b[0;34m            Optionally, import the `WeaviateTargetVectors` class from `st_weaviate_connection` to use this argument.\u001b[0m\n",
       "\u001b[0;34m        query_properties : List[str], optional\u001b[0m\n",
       "\u001b[0;34m            The properties to query in the keyword search part of the query.\u001b[0m\n",
       "\u001b[0;34m            If not provided, all properties are queried.\u001b[0m\n",
       "\u001b[0;34m        return_properties : List[str], optional\u001b[0m\n",
       "\u001b[0;34m            The properties to return in the query.\u001b[0m\n",
       "\u001b[0;34m            If not provided, all properties are returned, except for BLOBs.\u001b[0m\n",
       "\u001b[0;34m        alpha: float, optional\u001b[0m\n",
       "\u001b[0;34m            The weight of the semantic search part of the query. (alpha=1 is a semantic search, alpha=0 is a keyword search).\u001b[0m\n",
       "\u001b[0;34m            If not provided, the Weaviate server default value is used.\u001b[0m\n",
       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mcollection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcollection_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhybrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mquery\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mfilters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mtarget_vector\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget_vectors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mquery_properties\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquery_properties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mreturn_properties\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_properties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mweaviate_response_objects_to_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      ~/code/st-weaviate-connection/st_weaviate_connection/connection.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conn.query??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "For example, we can narrow down the results by the release date. Here is the same search, but only for movies from before 2010:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>budget</th>\n",
       "      <th>tagline</th>\n",
       "      <th>overview</th>\n",
       "      <th>revenue</th>\n",
       "      <th>title</th>\n",
       "      <th>release_year</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14337</td>\n",
       "      <td>2098</td>\n",
       "      <td>7000</td>\n",
       "      <td>What happens if it actually works?</td>\n",
       "      <td>Two fledgling inventors discover a complex met...</td>\n",
       "      <td>545436</td>\n",
       "      <td>Primer</td>\n",
       "      <td>2004</td>\n",
       "      <td>6.768</td>\n",
       "      <td>[Science Fiction, Drama, Thriller]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1124</td>\n",
       "      <td>15086</td>\n",
       "      <td>40000000</td>\n",
       "      <td>Are You Watching Closely?</td>\n",
       "      <td>A mysterious story of two magicians whose inte...</td>\n",
       "      <td>109676311</td>\n",
       "      <td>The Prestige</td>\n",
       "      <td>2006</td>\n",
       "      <td>8.203</td>\n",
       "      <td>[Drama, Mystery, Science Fiction]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34584</td>\n",
       "      <td>3786</td>\n",
       "      <td>27000000</td>\n",
       "      <td>A boy who needs a friend finds a world that ne...</td>\n",
       "      <td>While hiding from bullies in his school's atti...</td>\n",
       "      <td>20158808</td>\n",
       "      <td>The NeverEnding Story</td>\n",
       "      <td>1984</td>\n",
       "      <td>7.194</td>\n",
       "      <td>[Adventure, Fantasy, Family, Drama]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25376</td>\n",
       "      <td>2469</td>\n",
       "      <td>2000000</td>\n",
       "      <td>An unsolved crime. A love story. An unwritten ...</td>\n",
       "      <td>Hoping to put to rest years of unease concerni...</td>\n",
       "      <td>33965843</td>\n",
       "      <td>The Secret in Their Eyes</td>\n",
       "      <td>2009</td>\n",
       "      <td>8.002</td>\n",
       "      <td>[Mystery, Thriller, Drama]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1024</td>\n",
       "      <td>882</td>\n",
       "      <td>5000000</td>\n",
       "      <td>Not all angels are innocent.</td>\n",
       "      <td>Wealthy and precocious teenager Juliet transfe...</td>\n",
       "      <td>3049135</td>\n",
       "      <td>Heavenly Creatures</td>\n",
       "      <td>1994</td>\n",
       "      <td>7.000</td>\n",
       "      <td>[Drama, Fantasy]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id  vote_count    budget  \\\n",
       "0     14337        2098      7000   \n",
       "1      1124       15086  40000000   \n",
       "2     34584        3786  27000000   \n",
       "3     25376        2469   2000000   \n",
       "4      1024         882   5000000   \n",
       "\n",
       "                                             tagline  \\\n",
       "0                 What happens if it actually works?   \n",
       "1                          Are You Watching Closely?   \n",
       "2  A boy who needs a friend finds a world that ne...   \n",
       "3  An unsolved crime. A love story. An unwritten ...   \n",
       "4                       Not all angels are innocent.   \n",
       "\n",
       "                                            overview    revenue  \\\n",
       "0  Two fledgling inventors discover a complex met...     545436   \n",
       "1  A mysterious story of two magicians whose inte...  109676311   \n",
       "2  While hiding from bullies in his school's atti...   20158808   \n",
       "3  Hoping to put to rest years of unease concerni...   33965843   \n",
       "4  Wealthy and precocious teenager Juliet transfe...    3049135   \n",
       "\n",
       "                      title  release_year  vote_average  \\\n",
       "0                    Primer          2004         6.768   \n",
       "1              The Prestige          2006         8.203   \n",
       "2     The NeverEnding Story          1984         7.194   \n",
       "3  The Secret in Their Eyes          2009         8.002   \n",
       "4        Heavenly Creatures          1994         7.000   \n",
       "\n",
       "                                genres  \n",
       "0   [Science Fiction, Drama, Thriller]  \n",
       "1    [Drama, Mystery, Science Fiction]  \n",
       "2  [Adventure, Fantasy, Family, Drama]  \n",
       "3           [Mystery, Thriller, Drama]  \n",
       "4                     [Drama, Fantasy]  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from st_weaviate_connection import WeaviateFilter\n",
    "\n",
    "df = conn.query(\n",
    "    collection_name=\"MovieDemo\",\n",
    "    query=\"Fantasy or sci-fi drama\",\n",
    "    filters=WeaviateFilter.by_property(\"release_year\").less_than(2010)\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "If you prefer to use a raw GraphQL query, you can use the `.graphql_query` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-31 18:50:59.359 No runtime found, using MemoryCacheStorageManager\n",
      "2024-07-31 18:50:59.362 No runtime found, using MemoryCacheStorageManager\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_additional</th>\n",
       "      <th>overview</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'distance': 0.44417703}</td>\n",
       "      <td>A sumptuous and sensual tale of intrigue, roma...</td>\n",
       "      <td>The Other Boleyn Girl</td>\n",
       "      <td>6.685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'distance': 0.4576857}</td>\n",
       "      <td>An epic that details the checkered rise and fa...</td>\n",
       "      <td>Napoleon</td>\n",
       "      <td>6.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'distance': 0.47027773}</td>\n",
       "      <td>The timeless tale of King Arthur and the legen...</td>\n",
       "      <td>First Knight</td>\n",
       "      <td>6.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'distance': 0.4740405}</td>\n",
       "      <td>With Ran, legendary director Akira Kurosawa re...</td>\n",
       "      <td>Ran</td>\n",
       "      <td>8.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'distance': 0.47975093}</td>\n",
       "      <td>England, 15th century. Hal, a capricious princ...</td>\n",
       "      <td>The King</td>\n",
       "      <td>7.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'distance': 0.48198485}</td>\n",
       "      <td>Akira Kurosawa's lauded feudal epic presents t...</td>\n",
       "      <td>Kagemusha</td>\n",
       "      <td>7.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'distance': 0.4889692}</td>\n",
       "      <td>A fictional history of two legendary revolutio...</td>\n",
       "      <td>RRR</td>\n",
       "      <td>7.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'distance': 0.4907261}</td>\n",
       "      <td>Katherine Watson is a recent UCLA graduate hir...</td>\n",
       "      <td>Mona Lisa Smile</td>\n",
       "      <td>6.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'distance': 0.49372083}</td>\n",
       "      <td>The retelling of France’s iconic but ill-fated...</td>\n",
       "      <td>Marie Antoinette</td>\n",
       "      <td>6.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'distance': 0.4953565}</td>\n",
       "      <td>In 25 AD, Judah Ben-Hur, a Jew in ancient Jude...</td>\n",
       "      <td>Ben-Hur</td>\n",
       "      <td>7.889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                _additional  \\\n",
       "0  {'distance': 0.44417703}   \n",
       "1   {'distance': 0.4576857}   \n",
       "2  {'distance': 0.47027773}   \n",
       "3   {'distance': 0.4740405}   \n",
       "4  {'distance': 0.47975093}   \n",
       "5  {'distance': 0.48198485}   \n",
       "6   {'distance': 0.4889692}   \n",
       "7   {'distance': 0.4907261}   \n",
       "8  {'distance': 0.49372083}   \n",
       "9   {'distance': 0.4953565}   \n",
       "\n",
       "                                            overview                  title  \\\n",
       "0  A sumptuous and sensual tale of intrigue, roma...  The Other Boleyn Girl   \n",
       "1  An epic that details the checkered rise and fa...               Napoleon   \n",
       "2  The timeless tale of King Arthur and the legen...           First Knight   \n",
       "3  With Ran, legendary director Akira Kurosawa re...                    Ran   \n",
       "4  England, 15th century. Hal, a capricious princ...               The King   \n",
       "5  Akira Kurosawa's lauded feudal epic presents t...              Kagemusha   \n",
       "6  A fictional history of two legendary revolutio...                    RRR   \n",
       "7  Katherine Watson is a recent UCLA graduate hir...        Mona Lisa Smile   \n",
       "8  The retelling of France’s iconic but ill-fated...       Marie Antoinette   \n",
       "9  In 25 AD, Judah Ben-Hur, a Jew in ancient Jude...                Ben-Hur   \n",
       "\n",
       "   vote_average  \n",
       "0         6.685  \n",
       "1         6.428  \n",
       "2         6.079  \n",
       "3         8.078  \n",
       "4         7.153  \n",
       "5         7.805  \n",
       "6         7.759  \n",
       "7         6.935  \n",
       "8         6.695  \n",
       "9         7.889  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gql = \"\"\"\n",
    "{\n",
    "  Get {\n",
    "    MovieDemo (\n",
    "        limit: 10\n",
    "      nearText: {\n",
    "        concepts: [\"historical period film\"]\n",
    "      }\n",
    "    ) {\n",
    "      title\n",
    "      overview\n",
    "      vote_average\n",
    "      _additional {\n",
    "        distance\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "df = conn.graphql_query(gql)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Advanced Usage\n",
    "\n",
    "The Streamlit Weaviate connection is a thin wrapper around the Weaviate Python client. \n",
    "\n",
    "This means that you can use the Weaviate Python client directly to perform more advanced operations.\n",
    "\n",
    "We recommend using the client object in a context manager to ensure that no resources are leaked."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Example: Perform retrieval augmented generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Generated recommendation\n",
      "Based on the list provided, here are two movies that would be suitable and enjoyable for a family movie night: \n",
      "\n",
      "**\"The NeverEnding Story\"**\n",
      "Tagline: \"A boy who needs a friend finds a world that needs a hero in a land beyond imagination!\"\n",
      "This classic fantasy adventure is a wonderful choice for families. It follows a young boy who discovers a magical book and is transported to a fantastical world, where he embarks on a quest to save a kingdom from destruction. The movie is filled with exciting creatures, imaginative settings, and themes of friendship, courage, and the power of imagination. \n",
      "\n",
      "**\"Jack the Giant Slayer**\n",
      "Tagline: \"Prepare for a giant adventure\"\n",
      "This action-packed film is a fun twist on the classic fairy tale, \"Jack and the Beanstalk\". It tells the story of a young man who opens a gateway to a world of giants and must lead a rescue mission to save a kidnapped princess. While there is some fantasy violence, it is generally mild and suitable for older children. The movie emphasizes bravery, teamwork, and the importance of standing up for what is right, making it a great choice for a family audience. \n",
      "\n",
      "Both of these movies offer a mix of adventure, imagination, and positive messages, providing entertainment that the whole family can enjoy together!\n",
      "\n",
      "## Source data\n",
      "Title: Cloud Atlas\n",
      "Title: The Prestige\n",
      "Title: Ad Astra\n",
      "Title: Phenomenon\n",
      "Title: Upside Down\n",
      "Title: The Giver\n",
      "Title: Primer\n",
      "Title: Before I Fall\n",
      "Title: Moon\n",
      "Title: Her\n",
      "Title: The Man Who Fell to Earth\n",
      "Title: Heavenly Creatures\n",
      "Title: Enemy Mine\n",
      "Title: Through My Window\n",
      "Title: Ex Machina\n",
      "Title: Melancholia\n",
      "Title: Jack the Giant Slayer\n",
      "Title: Always\n",
      "Title: Close Encounters of the Third Kind\n",
      "Title: The NeverEnding Story\n"
     ]
    }
   ],
   "source": [
    "with conn.client() as client:\n",
    "    collection = client.collections.get(\"MovieDemo\")\n",
    "    response = collection.generate.hybrid(\n",
    "        limit=20,\n",
    "        query=\"Fantasy or sci-fi drama\",\n",
    "        grouped_task=\"From these, recommend one or two movies that would be family friendly!\",\n",
    "        grouped_properties=[\"title\", \"tagline\"],\n",
    "    )\n",
    "\n",
    "    print(\"## Generated recommendation\")\n",
    "    print(response.generated)\n",
    "    print(\"\\n## Source data\")\n",
    "    for o in response.objects:\n",
    "        print(f\"Title: {o.properties['title']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "See the [Weaviate Python client documentation](https://weaviate.io/developers/weaviate/client-libraries/python), and the [Weaviate documentation](https://weaviate.io/developers/weaviate/) for more information on the available operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Use a local Weaviate instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "To connect to a local instance instead, specify the `url` parameter as `\"localhost\"` as shown below.\n",
    "\n",
    "Then, you will be connected to the local Weaviate instance with default settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "conn = st.connection(\n",
    "    \"weaviate\",\n",
    "    type=WeaviateConnection,\n",
    "    url=\"localhost\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Using Secrets"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
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   },
   "source": [
    "The following are valid values you can specify in your `secrets.toml` file when using this connection:\n",
    "\n",
    "| Config | Description |\n",
    "| --- | --- |\n",
    "| WEAVIATE_URL | The url of the weavite instance you want to connect to |\n",
    "| WEAVIATE_API_KEY | The corresponding api key of the weaviate instance you want to connect to (if applicable) |\n",
    "\n",
    "For more details, refer to:\n",
    "\n",
    "* [How to use secrets management](https://docs.streamlit.io/library/advanced-features/secrets-management#how-to-use-secrets-management)\n",
    "* [st.connection](https://docs.streamlit.io/library/api-reference/connections/st.connection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Using the Weaviate client library directly\n",
    "\n",
    "You can also use the Weaviate client library directly to perform more advanced operations.\n",
    "\n",
    "Please see the [Weaviate Python client documentation](https://weaviate.io/developers/weaviate/client-libraries/python) for more information."
   ]
  }
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