mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
e1f4f9ac3e
Added missed pages for `integrations/providers` from `vectorstores`. Updated several `vectorstores` notebooks.
401 lines
11 KiB
Plaintext
401 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "683953b3",
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"metadata": {},
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"source": [
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"# ClickHouse\n",
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"\n",
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"> [ClickHouse](https://clickhouse.com/) is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like `L2Distance`) as well as [approximate nearest neighbor search indexes](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) enable ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL.\n",
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"\n",
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"This notebook shows how to use functionality related to the `ClickHouse` vector search."
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]
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},
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{
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"cell_type": "markdown",
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"id": "43ead5d5-2c1f-4dce-a69a-cb00e4f9d6f0",
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"metadata": {},
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"source": [
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"## Setting up envrionments"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2c434bc",
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"metadata": {},
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"source": [
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"Setting up local clickhouse server with docker (optional)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "249a7751",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:43:43.035606Z",
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"start_time": "2023-06-03T08:43:42.618531Z"
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}
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},
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"outputs": [],
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"source": [
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"! docker run -d -p 8123:8123 -p9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 clickhouse/clickhouse-server:23.4.2.11"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7bd3c1c0",
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"metadata": {},
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"source": [
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"Setup up clickhouse client driver"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9d614bf8",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install clickhouse-connect"
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]
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},
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{
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"cell_type": "markdown",
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"id": "15a1d477-9cdb-4d82-b019-96951ecb2b72",
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"metadata": {},
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"source": [
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"We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "91003ea5-0c8c-436c-a5de-aaeaeef2f458",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:49:35.383673Z",
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"start_time": "2023-06-03T08:49:33.984547Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"if not os.environ[\"OPENAI_API_KEY\"]:\n",
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" os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "aac9563e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:33:31.554934Z",
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"start_time": "2023-06-03T08:33:31.549590Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Clickhouse, ClickhouseSettings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a3c3999a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:33:32.527387Z",
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"start_time": "2023-06-03T08:33:32.501312Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "6e104aee",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:33:35.503823Z",
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"start_time": "2023-06-03T08:33:33.745832Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Inserting data...: 100%|██████████| 42/42 [00:00<00:00, 2801.49it/s]\n"
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]
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}
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],
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"source": [
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"for d in docs:\n",
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" d.metadata = {\"some\": \"metadata\"}\n",
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"settings = ClickhouseSettings(table=\"clickhouse_vector_search_example\")\n",
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"docsearch = Clickhouse.from_documents(docs, embeddings, config=settings)\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.similarity_search(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "9c608226",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"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"
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]
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}
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],
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"source": [
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"print(docs[0].page_content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e3a8b105",
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"metadata": {},
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"source": [
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"## Get connection info and data schema"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "69996818",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:28:58.252991Z",
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"start_time": "2023-06-03T08:28:58.197560Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[92m\u001b[1mdefault.clickhouse_vector_search_example @ localhost:8123\u001b[0m\n",
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"\n",
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"\u001b[1musername: None\u001b[0m\n",
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"\n",
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"Table Schema:\n",
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"---------------------------------------------------\n",
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"|\u001b[94mid \u001b[0m|\u001b[96mNullable(String) \u001b[0m|\n",
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"|\u001b[94mdocument \u001b[0m|\u001b[96mNullable(String) \u001b[0m|\n",
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"|\u001b[94membedding \u001b[0m|\u001b[96mArray(Float32) \u001b[0m|\n",
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"|\u001b[94mmetadata \u001b[0m|\u001b[96mObject('json') \u001b[0m|\n",
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"|\u001b[94muuid \u001b[0m|\u001b[96mUUID \u001b[0m|\n",
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"---------------------------------------------------\n",
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"\n"
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]
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}
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],
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"source": [
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"print(str(docsearch))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "324ac147",
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"metadata": {},
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"source": [
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"### Clickhouse table schema"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b5bd7c5b",
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"metadata": {},
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"source": [
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"> Clickhouse table will be automatically created if not exist by default. Advanced users could pre-create the table with optimized settings. For distributed Clickhouse cluster with sharding, table engine should be configured as `Distributed`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "54f4f561",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Clickhouse Table DDL:\n",
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"\n",
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"CREATE TABLE IF NOT EXISTS default.clickhouse_vector_search_example(\n",
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" id Nullable(String),\n",
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" document Nullable(String),\n",
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" embedding Array(Float32),\n",
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" metadata JSON,\n",
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" uuid UUID DEFAULT generateUUIDv4(),\n",
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" CONSTRAINT cons_vec_len CHECK length(embedding) = 1536,\n",
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" INDEX vec_idx embedding TYPE annoy(100,'L2Distance') GRANULARITY 1000\n",
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") ENGINE = MergeTree ORDER BY uuid SETTINGS index_granularity = 8192\n"
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]
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}
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],
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"source": [
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"print(f\"Clickhouse Table DDL:\\n\\n{docsearch.schema}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f59360c0",
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"metadata": {},
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"source": [
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"## Filtering\n",
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"\n",
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"You can have direct access to ClickHouse SQL where statement. You can write `WHERE` clause following standard SQL.\n",
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"\n",
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"**NOTE**: Please be aware of SQL injection, this interface must not be directly called by end-user.\n",
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"\n",
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"If you custimized your `column_map` under your setting, you search with filter like this:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "232055f6",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:29:36.680805Z",
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"start_time": "2023-06-03T08:29:34.963676Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Inserting data...: 100%|██████████| 42/42 [00:00<00:00, 6939.56it/s]\n"
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]
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}
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],
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"source": [
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"from langchain.vectorstores import Clickhouse, ClickhouseSettings\n",
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"\n",
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"for i, d in enumerate(docs):\n",
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" d.metadata = {\"doc_id\": i}\n",
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"\n",
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"docsearch = Clickhouse.from_documents(docs, embeddings)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "ddbcee77",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:29:43.487436Z",
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"start_time": "2023-06-03T08:29:43.040831Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.6779101415357189 {'doc_id': 0} Madam Speaker, Madam...\n",
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"0.6997970363474885 {'doc_id': 8} And so many families...\n",
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"0.7044504914336727 {'doc_id': 1} Groups of citizens b...\n",
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"0.7053558702165094 {'doc_id': 6} And I’m taking robus...\n"
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]
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}
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],
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"source": [
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"meta = docsearch.metadata_column\n",
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"output = docsearch.similarity_search_with_relevance_scores(\n",
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" \"What did the president say about Ketanji Brown Jackson?\",\n",
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" k=4,\n",
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" where_str=f\"{meta}.doc_id<10\",\n",
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")\n",
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"for d, dist in output:\n",
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" print(dist, d.metadata, d.page_content[:20] + \"...\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a359ed74",
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"metadata": {},
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"source": [
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"## Deleting your data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "fb6a9d36",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-03T08:30:24.822384Z",
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"start_time": "2023-06-03T08:30:24.798571Z"
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}
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},
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"outputs": [],
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"source": [
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"docsearch.drop()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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