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docs: Use Kinetica Sql context API (#21993)

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Update python notebook to use new Kinetica SQL context API.
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Chad Juliano 2024-05-22 16:26:20 -05:00 committed by GitHub
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1 changed files with 50 additions and 29 deletions
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75
docs/docs/integrations/chat/kinetica.ipynb
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@ -62,10 +62,10 @@
"%pip install --upgrade --quiet langchain-core langchain-community\n", "%pip install --upgrade --quiet langchain-core langchain-community\n",
"\n", "\n",
"# Install Kineitca DB connection package\n", "# Install Kineitca DB connection package\n",
"%pip install --upgrade --quiet gpudb typeguard\n", "%pip install --upgrade --quiet 'gpudb>=7.2.0.8' typeguard pandas tqdm\n",
"\n", "\n",
"# Install packages needed for this tutorial\n", "# Install packages needed for this tutorial\n",
"%pip install --upgrade --quiet faker" "%pip install --upgrade --quiet faker ipykernel "
] ]
}, },
{ {
@ -114,7 +114,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -139,11 +139,11 @@
"\n", "\n",
" birthdate \n", " birthdate \n",
"id \n", "id \n",
"0 1997-12-01 \n", "0 1997-12-08 \n",
"1 1924-07-27 \n", "1 1924-08-03 \n",
"2 1933-11-28 \n", "2 1933-12-05 \n",
"3 1988-10-19 \n", "3 1988-10-26 \n",
"4 1931-03-12 \n" "4 1931-03-19 \n"
] ]
} }
], ],
@ -222,39 +222,60 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CREATE OR REPLACE CONTEXT \"demo\".\"test_llm_ctx\" (\n",
" TABLE = \"demo\".\"user_profiles\",\n",
" COMMENT = 'Contains user profiles.'\n",
"),\n",
"(\n",
" SAMPLES = ( \n",
" 'How many male users are there?' = 'select count(1) as num_users\n",
" from demo.user_profiles\n",
" where sex = ''M'';' )\n",
")\n"
]
},
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"1" "1"
] ]
}, },
"execution_count": 4, "execution_count": 7,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# create an LLM context for the table.\n", "from gpudb import GPUdbSamplesClause, GPUdbSqlContext, GPUdbTableClause\n",
"\n", "\n",
"sql = f\"\"\"\n", "table_ctx = GPUdbTableClause(table=table_name, comment=\"Contains user profiles.\")\n",
"CREATE OR REPLACE CONTEXT {kinetica_ctx}\n", "\n",
"samples_ctx = GPUdbSamplesClause(\n",
" samples=[\n",
" (\n", " (\n",
" TABLE = {table_name}\n", " \"How many male users are there?\",\n",
" COMMENT = 'Contains user profiles.'\n", " f\"\"\"\n",
"),\n", " select count(1) as num_users\n",
"(\n",
" SAMPLES = (\n",
" 'How many male users are there?' = \n",
" 'select count(1) as num_users\n",
" from {table_name}\n", " from {table_name}\n",
" where sex = ''M'';')\n", " where sex = 'M';\n",
" \"\"\",\n",
" )\n",
" ]\n",
")\n", ")\n",
"\"\"\"\n",
"\n", "\n",
"count_affected = kinetica_llm.kdbc.execute(sql)\n", "context_sql = GPUdbSqlContext(\n",
" name=kinetica_ctx, tables=[table_ctx], samples=samples_ctx\n",
").build_sql()\n",
"\n",
"print(context_sql)\n",
"count_affected = kinetica_llm.kdbc.execute(context_sql)\n",
"count_affected" "count_affected"
] ]
}, },
@ -273,7 +294,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -334,7 +355,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -357,7 +378,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -404,7 +425,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.19" "version": "3.9.19"
} }
}, },
"nbformat": 4, "nbformat": 4,