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477 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1446a9ca",
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"metadata": {},
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"source": [
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"# DingoDB\n",
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"\n",
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">[DingoDB](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.\n",
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"\n",
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"In the walkthrough, we'll demo the `SelfQueryRetriever` with a `DingoDB` 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": "43c61487",
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"metadata": {},
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"source": [
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"## Creating a DingoDB index\n",
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"First we'll want to create a `DingoDB` vector store 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 DingoDB, you should have a [DingoDB instance up and running](https://github.com/dingodb/dingo-deploy/blob/main/README.md).\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": null,
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"id": "f84c227f",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet dingodb\n",
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"# or install latest:\n",
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"%pip install --upgrade --quiet git+https://git@github.com/dingodb/pydingo.git"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5fdf04ae",
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"metadata": {},
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"source": [
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"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "727dce3d",
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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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"\n",
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"OPENAI_API_KEY = \"\"\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c39cd415",
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"metadata": {},
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"source": []
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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": "cb4a5787",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.schema import Document\n",
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"from langchain_community.vectorstores import Dingo\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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"# create new index\n",
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"from dingodb import DingoDB\n",
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"\n",
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"index_name = \"langchain_demo\"\n",
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"\n",
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"dingo_client = DingoDB(user=\"\", password=\"\", host=[\"172.30.14.221:13000\"])\n",
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"# First, check if our index already exists. If it doesn't, we create it\n",
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"if (\n",
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" index_name not in dingo_client.get_index()\n",
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" and index_name.upper() not in dingo_client.get_index()\n",
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"):\n",
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" # we create a new index, modify to your own\n",
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" dingo_client.create_index(\n",
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" index_name=index_name, dimension=1536, metric_type=\"cosine\", auto_id=False\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": "2a1a6078-e959-4069-90d4-2e5a40d9fda4",
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"metadata": {},
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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\", \"science fiction\"'},\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\", \"thriller\"',\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 = Dingo.from_documents(\n",
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" docs, embeddings, index_name=index_name, client=dingo_client\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9dbe93f4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 3,
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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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"dingo_client.get_index()\n",
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"dingo_client.delete_index(\"langchain_demo\")"
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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": "fa0b2921",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"9"
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]
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},
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"execution_count": 5,
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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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"dingo_client.vector_count(\"langchain_demo\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5ecaab6d",
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"metadata": {},
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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": 6,
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"id": "86e34dbf",
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"metadata": {},
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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 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 = OpenAI(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": "ea9df8d4",
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"metadata": {},
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"source": [
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"## Testing it out\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": 7,
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"id": "38a126e9",
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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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"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={'id': 1183188982475, 'text': 'A bunch of scientists bring back dinosaurs and mayhem breaks loose', 'score': 0.13397777, 'year': {'value': 1993}, 'rating': {'value': 7.7}, 'genre': '\"action\", \"science fiction\"'}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 1183189196391, 'text': 'Toys come alive and have a blast doing so', 'score': 0.18994397, 'year': {'value': 1995}, 'genre': 'animated'}),\n",
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" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'id': 1183189220159, 'text': 'Three men walk into the Zone, three men walk out of the Zone', 'score': 0.23288351, 'year': {'value': 1979}, 'director': 'Andrei Tarkovsky', 'rating': {'value': 9.9}, 'genre': '\"science fiction\", \"thriller\"'}),\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={'id': 1183189148854, 'text': 'A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', 'score': 0.24421334, 'year': {'value': 2006}, 'director': 'Satoshi Kon', 'rating': {'value': 8.6}})]"
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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 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": 8,
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"id": "fc3f1e6e",
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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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"query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None\n",
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"comparator=<Comparator.GT: 'gt'> attribute='rating' value=8.5\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={'id': 1183189220159, 'text': 'Three men walk into the Zone, three men walk out of the Zone', 'score': 0.25033575, 'year': {'value': 1979}, 'director': 'Andrei Tarkovsky', 'genre': '\"science fiction\", \"thriller\"', 'rating': {'value': 9.9}}),\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={'id': 1183189148854, 'text': 'A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', 'score': 0.26431882, 'year': {'value': 2006}, 'director': 'Satoshi Kon', 'rating': {'value': 8.6}})]"
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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 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": 9,
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"id": "b19d4da0",
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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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"query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n",
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"comparator=<Comparator.EQ: 'eq'> attribute='director' value='Greta Gerwig'\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={'id': 1183189172623, 'text': 'A bunch of normal-sized women are supremely wholesome and some men pine after them', 'score': 0.19482517, 'year': {'value': 2019}, 'director': 'Greta Gerwig', 'rating': {'value': 8.3}})]"
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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 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": 13,
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"id": "f900e40e",
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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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"query='science fiction' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None\n",
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"comparator=<Comparator.GT: 'gt'> attribute='rating' value=8.5\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 psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'id': 1183189148854, 'text': 'A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', 'score': 0.19805312, 'year': {'value': 2006}, 'director': 'Satoshi Kon', 'rating': {'value': 8.6}}),\n",
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" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'id': 1183189220159, 'text': 'Three men walk into the Zone, three men walk out of the Zone', 'score': 0.225586, 'year': {'value': 1979}, 'director': 'Andrei Tarkovsky', 'rating': {'value': 9.9}, 'genre': '\"science fiction\", \"thriller\"'})]"
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]
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},
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"execution_count": 13,
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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": "code",
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"execution_count": 12,
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"id": "12a51522",
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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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"query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005)]), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')]) limit=None\n",
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"operator=<Operator.AND: 'and'> arguments=[Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005)]), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')]\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='Toys come alive and have a blast doing so', metadata={'id': 1183189196391, 'text': 'Toys come alive and have a blast doing so', 'score': 0.133829, 'year': {'value': 1995}, 'genre': 'animated'})]"
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]
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},
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"execution_count": 12,
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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 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 toys, and preferably is animated\"\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": "6fe7536c",
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"metadata": {},
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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": 14,
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"id": "3a2937c2",
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"metadata": {},
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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": 15,
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"id": "83d233aa",
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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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"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={'id': 1183188982475, 'text': 'A bunch of scientists bring back dinosaurs and mayhem breaks loose', 'score': 0.13394928, 'year': {'value': 1993}, 'rating': {'value': 7.7}, 'genre': '\"action\", \"science fiction\"'}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 1183189196391, 'text': 'Toys come alive and have a blast doing so', 'score': 0.1899159, 'year': {'value': 1995}, 'genre': 'animated'})]"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# This example only specifies a relevant query\n",
|
|
"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": "3.11.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|