mirror of
https://github.com/hwchase17/langchain.git
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366 lines
10 KiB
Plaintext
366 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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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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"# MyScale\n",
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"\n",
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">[MyScale](https://docs.myscale.com/en/overview/) is a cloud-based database optimized for AI applications and solutions, built on the open-source [ClickHouse](https://github.com/ClickHouse/ClickHouse). \n",
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"\n",
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"This notebook shows how to use functionality related to the `MyScale` vector database."
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]
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},
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{
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"attachments": {},
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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 environments"
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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": "7dccc580-8270-4714-ad61-f79783dd6eea",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet clickhouse-connect"
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]
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},
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{
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"attachments": {},
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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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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
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"os.environ[\"OPENAI_API_BASE\"] = getpass.getpass(\"OpenAI Base:\")\n",
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"os.environ[\"MYSCALE_HOST\"] = getpass.getpass(\"MyScale Host:\")\n",
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"os.environ[\"MYSCALE_PORT\"] = getpass.getpass(\"MyScale Port:\")\n",
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"os.environ[\"MYSCALE_USERNAME\"] = getpass.getpass(\"MyScale Username:\")\n",
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"os.environ[\"MYSCALE_PASSWORD\"] = getpass.getpass(\"MyScale Password:\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "a9d16fa3",
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"metadata": {},
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"source": [
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"There are two ways to set up parameters for myscale index.\n",
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"\n",
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"1. Environment Variables\n",
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"\n",
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" Before you run the app, please set the environment variable with `export`:\n",
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" `export MYSCALE_HOST='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`\n",
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"\n",
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" You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)\n",
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"\n",
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" Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.\n",
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"\n",
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"2. Create `MyScaleSettings` object with parameters\n",
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"\n",
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"\n",
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" ```python\n",
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" from langchain_community.vectorstores import MyScale, MyScaleSettings\n",
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" config = MyScaleSetting(host=\"<your-backend-url>\", port=8443, ...)\n",
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" index = MyScale(embedding_function, config)\n",
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" index.add_documents(...)\n",
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" ```"
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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": "aac9563e",
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"metadata": {
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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_community.document_loaders import TextLoader\n",
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"from langchain_community.vectorstores import MyScale\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"from langchain_text_splitters import CharacterTextSplitter"
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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": "a3c3999a",
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"metadata": {
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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_community.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../modules/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": 5,
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"id": "6e104aee",
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"metadata": {},
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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 [00:15<00:00, 2.66it/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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"docsearch = MyScale.from_documents(docs, embeddings)\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": 7,
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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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"attachments": {},
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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": null,
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"id": "69996818",
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"metadata": {},
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"outputs": [],
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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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"attachments": {},
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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 myscale 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 customized 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": 7,
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"id": "232055f6",
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"metadata": {},
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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 [00:15<00:00, 2.68it/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_community.document_loaders import TextLoader\n",
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"from langchain_community.vectorstores import MyScale\n",
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"\n",
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"loader = TextLoader(\"../../modules/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 = MyScale.from_documents(docs, embeddings)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "8d867b05",
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"metadata": {},
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"source": [
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"### Similarity search with score"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "9ec25cc5",
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"metadata": {},
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"source": [
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"The returned distance score is cosine distance. Therefore, a lower score is better."
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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": "ddbcee77",
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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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"0.229655921459198 {'doc_id': 0} Madam Speaker, Madam...\n",
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"0.24506962299346924 {'doc_id': 8} And so many families...\n",
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"0.24786919355392456 {'doc_id': 1} Groups of citizens b...\n",
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"0.24875116348266602 {'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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"attachments": {},
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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\n",
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"\n",
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"You can either drop the table with `.drop()` method or partially delete your data with `.delete()` method."
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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": "3a0cc43b",
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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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"0.24506962299346924 {'doc_id': 8} And so many families...\n",
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"0.24875116348266602 {'doc_id': 6} And I’m taking robus...\n",
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"0.26027143001556396 {'doc_id': 7} We see the unity amo...\n",
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"0.26390212774276733 {'doc_id': 9} And unlike the $2 Tr...\n"
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]
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}
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],
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"source": [
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"# use directly a `where_str` to delete\n",
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"docsearch.delete(where_str=f\"{docsearch.metadata_column}.doc_id < 5\")\n",
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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": "code",
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"execution_count": 10,
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"id": "fb6a9d36",
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"metadata": {},
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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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"cell_type": "code",
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"execution_count": null,
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"id": "48dbd8e0",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.11.3"
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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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