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484 lines
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484 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "13afcae7",
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"metadata": {},
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"source": [
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"# Deep Lake\n",
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"\n",
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">[Deep Lake](https://www.activeloop.ai) is a multimodal database for building AI applications\n",
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">[Deep Lake](https://github.com/activeloopai/deeplake) is a database for AI.\n",
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">Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version,\n",
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"> & visualize any AI data. Stream data in real time to PyTorch/TensorFlow.\n",
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"\n",
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"In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `Deep Lake` vector store. "
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "68e75fb9",
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"metadata": {},
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"source": [
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"## Creating a Deep Lake vector store\n",
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"First we'll want to create a Deep Lake 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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"**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `deeplake` package."
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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": "a798fe66",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet lark"
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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": "43821a8e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# in case if some queries fail consider installing libdeeplake manually\n",
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"%pip install --upgrade --quiet libdeeplake"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
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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": 4,
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"id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
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"os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop 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": 5,
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"id": "cb4a5787",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain_community.vectorstores import DeepLake\n",
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"from langchain_core.documents import Document\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "bcbe04d9",
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"metadata": {
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"tags": []
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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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"Your Deep Lake dataset has been successfully created!\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/"
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]
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},
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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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"Dataset(path='hub://adilkhan/self_queery', tensors=['embedding', 'id', 'metadata', 'text'])\n",
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"\n",
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" tensor htype shape dtype compression\n",
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" ------- ------- ------- ------- ------- \n",
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" embedding embedding (6, 1536) float32 None \n",
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" id text (6, 1) str None \n",
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" metadata json (6, 1) str None \n",
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" text text (6, 1) str None \n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" \r"
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]
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}
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],
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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\": \"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\",\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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"username_or_org = \"<USERNAME_OR_ORG>\"\n",
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"vectorstore = DeepLake.from_documents(\n",
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" docs,\n",
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" embeddings,\n",
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" dataset_path=f\"hub://{username_or_org}/self_queery\",\n",
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" overwrite=True,\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "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": 7,
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"id": "c90b0b40",
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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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"attachments": {},
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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": 8,
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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": "stderr",
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"output_type": "stream",
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"text": [
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"/home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/chains/llm.py:279: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
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" warnings.warn(\n"
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]
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},
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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='dinosaur' 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': 7.7, 'genre': 'science fiction'}),\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='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', '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': 8.6})]"
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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 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": 9,
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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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]
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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={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),\n",
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" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', '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 only specifies a filter\n",
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"retriever.invoke(\"I want to watch a movie rated higher than 8.5\")\n",
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"\n",
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"# in case if this example errored out, consider installing libdeeplake manually: `pip install libdeeplake`, and then restart notebook."
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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": "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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]
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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': 8.3})]"
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]
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},
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"execution_count": 10,
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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": 11,
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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=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction')]) 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, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]"
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]
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},
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"execution_count": 11,
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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=[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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]
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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={'year': 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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"attachments": {},
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|
"cell_type": "markdown",
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|
"id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
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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": 13,
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"id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"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": 14,
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|
"id": "2758d229-4f97-499c-819f-888acaf8ee10",
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"metadata": {
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"tags": []
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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='dinosaur' 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': 7.7, 'genre': 'science fiction'}),\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": 14,
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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 two movies about dinosaurs\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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|
"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|