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549 lines
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549 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1ad7250ddd99fba9",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"# Databricks Vector Search\n",
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"\n",
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">[Databricks Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html) is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.\n",
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"\n",
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"\n",
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"In the walkthrough, we'll demo the `SelfQueryRetriever` with a Databricks 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": "209652d4ab38ba7f",
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"metadata": {
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|
"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"## create Databricks vector store index\n",
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"First we'll want to create a databricks vector store index and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
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"\n",
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"**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`) along with integration-specific requirements."
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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": "b68da3303b0625f2",
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"metadata": {
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|
"ExecuteTime": {
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"end_time": "2024-03-29T02:39:28.887634Z",
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"start_time": "2024-03-29T02:39:27.277978Z"
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},
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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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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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install --upgrade --quiet langchain-core databricks-vectorsearch langchain-openai tiktoken"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a1113af6008f3f3d",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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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": 2,
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"id": "c243e15bcf72d539",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-29T02:40:59.788206Z",
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"start_time": "2024-03-29T02:40:59.783798Z"
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},
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"OpenAI API Key: ········\n",
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"Databricks host: ········\n",
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"Databricks token: ········\n"
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]
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}
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],
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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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"databricks_host = getpass.getpass(\"Databricks host:\")\n",
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"databricks_token = getpass.getpass(\"Databricks token:\")"
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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": 13,
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"id": "fd0c70c0be7d7130",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-29T02:42:28.467682Z",
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"start_time": "2024-03-29T02:42:21.255335Z"
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},
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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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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"[NOTICE] Using a Personal Authentication Token (PAT). Recommended for development only. For improved performance, please use Service Principal based authentication. To disable this message, pass disable_notice=True to VectorSearchClient().\n"
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]
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}
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],
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"source": [
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"from databricks.vector_search.client import VectorSearchClient\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"emb_dim = len(embeddings.embed_query(\"hello\"))\n",
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"\n",
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"vector_search_endpoint_name = \"vector_search_demo_endpoint\"\n",
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"\n",
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"\n",
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"vsc = VectorSearchClient(\n",
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" workspace_url=databricks_host, personal_access_token=databricks_token\n",
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")\n",
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"vsc.create_endpoint(name=vector_search_endpoint_name, endpoint_type=\"STANDARD\")"
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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": 14,
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"id": "3ead3943-7dd6-448c-bead-01157a000221",
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"metadata": {},
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"outputs": [],
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"source": [
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"index_name = \"udhay_demo.10x.demo_index\"\n",
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"\n",
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"index = vsc.create_direct_access_index(\n",
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" endpoint_name=vector_search_endpoint_name,\n",
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" index_name=index_name,\n",
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" primary_key=\"id\",\n",
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" embedding_dimension=emb_dim,\n",
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" embedding_vector_column=\"text_vector\",\n",
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" schema={\n",
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" \"id\": \"string\",\n",
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" \"page_content\": \"string\",\n",
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" \"year\": \"int\",\n",
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" \"rating\": \"float\",\n",
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" \"genre\": \"string\",\n",
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" \"text_vector\": \"array<float>\",\n",
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" },\n",
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")\n",
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"\n",
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"index.describe()"
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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": 15,
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"id": "3e62fc39-51d9-4757-a449-f543638b3cd1",
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"metadata": {},
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"outputs": [],
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"source": [
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"index = vsc.get_index(endpoint_name=vector_search_endpoint_name, index_name=index_name)\n",
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"\n",
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"index.describe()"
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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": "13863677-8123-4b36-82bc-2c28ee2a90fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.documents import Document\n",
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"\n",
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"docs = [\n",
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" Document(\n",
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" page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
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" metadata={\"id\": 1, \"year\": 1993, \"rating\": 7.7, \"genre\": \"action\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
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" metadata={\"id\": 2, \"year\": 2010, \"genre\": \"thriller\", \"rating\": 8.2},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
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" metadata={\"id\": 3, \"year\": 2019, \"rating\": 8.3, \"genre\": \"drama\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
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" metadata={\"id\": 4, \"year\": 1979, \"rating\": 9.9, \"genre\": \"science fiction\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
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" metadata={\"id\": 5, \"year\": 2006, \"genre\": \"thriller\", \"rating\": 9.0},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Toys come alive and have a blast doing so\",\n",
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" metadata={\"id\": 6, \"year\": 1995, \"genre\": \"animated\", \"rating\": 9.3},\n",
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" ),\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": 16,
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"id": "6fdc8f55-5b4c-4506-97ac-59d9b9ef8ffc",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.vectorstores import DatabricksVectorSearch\n",
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"\n",
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"vector_store = DatabricksVectorSearch(\n",
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" index,\n",
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" text_column=\"page_content\",\n",
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" embedding=embeddings,\n",
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" columns=[\"year\", \"rating\", \"genre\"],\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": 10,
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"id": "826375af-3fd7-4d41-9c7b-c273653c46b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store.add_documents(docs)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3810b731a981a957",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"## Creating our self-querying retriever\n",
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"Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
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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": 17,
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"id": "7095b68ea997468c",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-29T02:42:37.901230Z",
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"start_time": "2024-03-29T02:42:36.836827Z"
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},
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"collapsed": false,
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|
"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"from langchain.chains.query_constructor.base import AttributeInfo\n",
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"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
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"from langchain_openai import OpenAI\n",
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"\n",
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"metadata_field_info = [\n",
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" AttributeInfo(\n",
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" name=\"genre\",\n",
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" description=\"The genre of the movie\",\n",
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" type=\"string\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"year\",\n",
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" description=\"The year the movie was released\",\n",
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" type=\"integer\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
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" ),\n",
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"]\n",
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"document_content_description = \"Brief summary of a movie\"\n",
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"llm = OpenAI(temperature=0)\n",
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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm, vector_store, document_content_description, metadata_field_info, verbose=True\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "65ff2054be9d5236",
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|
"metadata": {
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|
"collapsed": false,
|
|
"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"## Test it out\n",
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"And now we can try actually using our retriever!\n"
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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": 18,
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|
"id": "267e2a68f26505b1",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-29T02:42:51.526470Z",
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|
"start_time": "2024-03-29T02:42:48.328191Z"
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},
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|
"collapsed": false,
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|
"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
|
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{
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"data": {
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"text/plain": [
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"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993.0, 'rating': 7.7, 'genre': 'action', 'id': 1.0}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995.0, 'rating': 9.3, 'genre': 'animated', 'id': 6.0}),\n",
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" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979.0, 'rating': 9.9, 'genre': 'science fiction', 'id': 4.0}),\n",
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" Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006.0, 'rating': 9.0, 'genre': 'thriller', 'id': 5.0})]"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a relevant query\n",
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"retriever.invoke(\"What are some movies about dinosaurs\")"
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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": 19,
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|
"id": "3afd98ca20782dda",
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"metadata": {
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"ExecuteTime": {
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|
"end_time": "2024-03-29T02:42:55.179002Z",
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"start_time": "2024-03-29T02:42:53.057022Z"
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},
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|
"collapsed": false,
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|
"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995.0, 'rating': 9.3, 'genre': 'animated', 'id': 6.0}),\n",
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" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979.0, 'rating': 9.9, 'genre': 'science fiction', 'id': 4.0})]"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example specifies a filter\n",
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"retriever.invoke(\"What are some highly rated movies (above 9)?\")"
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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": 20,
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|
"id": "9974f641e11abfe8",
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|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-29T02:42:58.472620Z",
|
|
"start_time": "2024-03-29T02:42:56.131594Z"
|
|
},
|
|
"collapsed": false,
|
|
"jupyter": {
|
|
"outputs_hidden": false
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|
}
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},
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"outputs": [
|
|
{
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|
"data": {
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|
"text/plain": [
|
|
"[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006.0, 'rating': 9.0, 'genre': 'thriller', 'id': 5.0}),\n",
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" Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010.0, 'rating': 8.2, 'genre': 'thriller', 'id': 2.0})]"
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]
|
|
},
|
|
"execution_count": 20,
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|
"metadata": {},
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"output_type": "execute_result"
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|
}
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],
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"source": [
|
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"# This example specifies both a relevant query and a filter\n",
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"retriever.invoke(\"What are the thriller movies that are highly rated?\")"
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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": 21,
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|
"id": "edd31040-ede0-40bb-bfcd-962118df4ffb",
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|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993.0, 'rating': 7.7, 'genre': 'action', 'id': 1.0})]"
|
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]
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},
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|
"execution_count": 21,
|
|
"metadata": {},
|
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"output_type": "execute_result"
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|
}
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|
],
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"source": [
|
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"# This example specifies a query and composite filter\n",
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"retriever.invoke(\n",
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" \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n",
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" and preferably has a lot of action\"\n",
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")"
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]
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},
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{
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|
"cell_type": "markdown",
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|
"id": "be593d3a6c508517",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"jupyter": {
|
|
"outputs_hidden": false
|
|
}
|
|
},
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"source": [
|
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"## Filter k\n",
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"\n",
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"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
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"\n",
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"We can do this by passing `enable_limit=True` to the constructor."
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]
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|
},
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|
{
|
|
"cell_type": "markdown",
|
|
"id": "7e17a10f-4187-4164-ab8f-b427c6b86cc0",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"jupyter": {
|
|
"outputs_hidden": false
|
|
}
|
|
},
|
|
"source": [
|
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"## Filter k\n",
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"\n",
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"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
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"\n",
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"We can do this by passing `enable_limit=True` to the constructor."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "e255b69c937fa424",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-29T02:43:02.779337Z",
|
|
"start_time": "2024-03-29T02:43:02.759900Z"
|
|
},
|
|
"collapsed": false,
|
|
"jupyter": {
|
|
"outputs_hidden": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"retriever = SelfQueryRetriever.from_llm(\n",
|
|
" llm,\n",
|
|
" vector_store,\n",
|
|
" document_content_description,\n",
|
|
" metadata_field_info,\n",
|
|
" verbose=True,\n",
|
|
" enable_limit=True,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "45674137c7f8a9d",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-29T02:43:07.357830Z",
|
|
"start_time": "2024-03-29T02:43:04.854323Z"
|
|
},
|
|
"collapsed": false,
|
|
"jupyter": {
|
|
"outputs_hidden": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"retriever.invoke(\"What are two movies about dinosaurs?\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.16"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|