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# Scores in Vectorestores' Docs Are Explained Following vectorestores can return scores with similar documents by using `similarity_search_with_score`: - chroma - docarray_hnsw - docarray_in_memory - faiss - myscale - qdrant - supabase - vectara - weaviate However, in documents, these scores were either not explained at all or explained in a way that could lead to misunderstandings (e.g., FAISS). For instance in FAISS document: if we consider the score returned by the function as a similarity score, we understand that a document returning a higher score is more similar to the source document. However, since the scores returned by the function are distance scores, we should understand that smaller scores correspond to more similar documents. For the libraries other than Vectara, I wrote the scores they use by investigating from the source libraries. Since I couldn't be certain about the score metric used by Vectara, I didn't make any changes in its documentation. The links mentioned in Vectara's documentation became broken due to updates, so I replaced them with working ones. VectorStores / Retrievers / Memory - @dev2049 my twitter: [berkedilekoglu](https://twitter.com/berkedilekoglu) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
333 lines
9.1 KiB
Plaintext
333 lines
9.1 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 envrionments"
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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 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": null,
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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 os\n",
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"import getpass\n",
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"\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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"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_URL='<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.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": null,
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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.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import MyScale\n",
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"from langchain.document_loaders import TextLoader"
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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": "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.document_loaders import TextLoader\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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"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 [00:18<00:00, 2.21it/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": 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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"As Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
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"\n",
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"It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. \n",
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"\n",
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"And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. \n",
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"\n",
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"Third, support our veterans. \n",
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"\n",
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"Veterans are the best of us. \n",
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"\n",
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"I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
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"\n",
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"My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free. \n",
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"\n",
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"Our troops in Iraq and Afghanistan faced many dangers.\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 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": 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, 2.69it/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 MyScale, MyScaleSettings\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 = 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": 16,
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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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"0.252379834651947 {'doc_id': 6, 'some': ''} And I’m taking robus...\n",
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"0.25022566318511963 {'doc_id': 1, 'some': ''} Groups of citizens b...\n",
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"0.2469480037689209 {'doc_id': 8, 'some': ''} And so many families...\n",
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"0.2428302764892578 {'doc_id': 0, 'some': 'metadata'} As Frances Haugen, w...\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('What did the president say about Ketanji Brown Jackson?', \n",
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" k=4, where_str=f\"{meta}.doc_id<10\")\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"
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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": "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": "3.10.6"
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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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