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508 lines
15 KiB
Plaintext
508 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "59895c73d1a0f3ca",
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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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"# DashVector\n",
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"\n",
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"> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n",
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"> The vector retrieval service `DashVector` is based on the `Proxima` core of the efficient vector engine independently developed by `DAMO Academy`,\n",
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"> and provides a cloud-native, fully managed vector retrieval service with horizontal expansion capabilities.\n",
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"> `DashVector` exposes its powerful vector management, vector query and other diversified capabilities through a simple and\n",
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"> easy-to-use SDK/API interface, which can be quickly integrated by upper-layer AI applications, thereby providing services\n",
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"> including large model ecology, multi-modal AI search, molecular structure A variety of application scenarios, including analysis,\n",
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"> provide the required efficient vector retrieval capabilities.\n",
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"\n",
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"In this notebook, we'll demo the `SelfQueryRetriever` with a `DashVector` vector store."
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]
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},
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{
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"cell_type": "markdown",
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"id": "539ae9367e45a178",
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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 DashVector vectorstore\n",
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"\n",
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"First we'll want to create a `DashVector` VectorStore 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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"To use DashVector, you have to have `dashvector` package installed, and you must have an API key and an Environment. Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html).\n",
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"\n",
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"NOTE: The self-query retriever requires you to have `lark` package installed."
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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": "67df7e1f8dc8cdd0",
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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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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet lark dashvector"
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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": "ff61eaf13973b5fe",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-08-24T02:58:46.905337Z",
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"start_time": "2023-08-24T02:58:46.252566Z"
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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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"import os\n",
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"\n",
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"import dashvector\n",
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"\n",
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"client = dashvector.Client(api_key=os.environ[\"DASHVECTOR_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": "de5c77957ee42d14",
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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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"outputs": [],
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"source": [
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"from langchain_community.embeddings import DashScopeEmbeddings\n",
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"from langchain_community.vectorstores import DashVector\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"embeddings = DashScopeEmbeddings()\n",
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"\n",
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"# create DashVector collection\n",
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"client.create(\"langchain-self-retriever-demo\", dimension=1536)"
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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": "8f40605548a4550",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:08.090031Z",
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"start_time": "2023-08-24T02:59:05.660295Z"
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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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"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={\"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={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\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={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\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={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\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={\"year\": 1995, \"genre\": \"animated\"},\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={\n",
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" \"year\": 1979,\n",
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" \"director\": \"Andrei Tarkovsky\",\n",
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" \"genre\": \"science fiction\",\n",
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" \"rating\": 9.9,\n",
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" },\n",
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" ),\n",
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"]\n",
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"vectorstore = DashVector.from_documents(\n",
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" docs, embeddings, collection_name=\"langchain-self-retriever-demo\"\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": "eb1340adafac8993",
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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 your self-querying retriever\n",
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"\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": 4,
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"id": "d65233dc044f95a7",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:11.003940Z",
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"start_time": "2023-08-24T02:59:10.476722Z"
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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_community.llms import Tongyi\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 or list[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=\"director\",\n",
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" description=\"The name of the movie director\",\n",
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" type=\"string\",\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 = Tongyi(temperature=0)\n",
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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm, vectorstore, 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": "a54af0d67b473db6",
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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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"## Testing it out\n",
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"\n",
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"And now we can try actually using our retriever!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "dad9da670a267fe7",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:28.577901Z",
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"start_time": "2023-08-24T02:59:26.780184Z"
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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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"query='dinosaurs' filter=None limit=None\n"
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]
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},
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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, 'rating': 7.699999809265137, 'genre': 'action'}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\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, 'director': 'Christopher Nolan', 'rating': 8.199999809265137}),\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, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
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]
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},
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"execution_count": 6,
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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": 7,
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"id": "d486a64316153d52",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:32.370774Z",
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"start_time": "2023-08-24T02:59:30.614252Z"
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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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"query=' ' filter=Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'}),\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, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
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]
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},
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"execution_count": 7,
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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 filter\n",
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"retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
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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": "e05919cdead7bd4a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:35.353439Z",
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"start_time": "2023-08-24T02:59:33.278255Z"
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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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"query='Greta Gerwig' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
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]
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},
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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 normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.300000190734863})]"
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]
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},
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"execution_count": 8,
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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 query and a filter\n",
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"retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
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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": "ac2c7012379e918e",
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"metadata": {
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"ExecuteTime": {
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|
"end_time": "2023-08-24T02:59:38.913707Z",
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"start_time": "2023-08-24T02:59:36.659271Z"
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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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"query='science fiction' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)]) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'})]"
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]
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},
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"execution_count": 9,
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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 composite filter\n",
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"retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
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]
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},
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{
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"cell_type": "markdown",
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|
"id": "af6aa93ae44af414",
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|
"metadata": {
|
|
"collapsed": false,
|
|
"jupyter": {
|
|
"outputs_hidden": false
|
|
}
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|
},
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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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{
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"cell_type": "code",
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"execution_count": 10,
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|
"id": "a8c8f09bf5702767",
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|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2023-08-24T02:59:41.594073Z",
|
|
"start_time": "2023-08-24T02:59:41.563323Z"
|
|
},
|
|
"collapsed": false,
|
|
"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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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm,\n",
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" vectorstore,\n",
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" document_content_description,\n",
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" metadata_field_info,\n",
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" enable_limit=True,\n",
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" 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": "code",
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|
"execution_count": 11,
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"id": "b1089a6043980b84",
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|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2023-08-24T02:59:48.450506Z",
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|
"start_time": "2023-08-24T02:59:46.252944Z"
|
|
},
|
|
"collapsed": false,
|
|
"jupyter": {
|
|
"outputs_hidden": false
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
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|
"text": [
|
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"query='dinosaurs' filter=None limit=2\n"
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]
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},
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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, 'rating': 7.699999809265137, 'genre': 'action'}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
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]
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},
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"execution_count": 11,
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|
"metadata": {},
|
|
"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 two 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": null,
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|
"id": "6d2d64e2ebb17d30",
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|
"metadata": {
|
|
"collapsed": false,
|
|
"jupyter": {
|
|
"outputs_hidden": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
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"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
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"language": "python",
|
|
"name": "python3"
|
|
},
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"language_info": {
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|
"codemirror_mode": {
|
|
"name": "ipython",
|
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"version": 3
|
|
},
|
|
"file_extension": ".py",
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|
"mimetype": "text/x-python",
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|
"name": "python",
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|
"nbconvert_exporter": "python",
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|
"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
|
|
}
|