mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
f907b62526
# Scores in Vectorestores' Docs Are Explained Following vectorestores can return scores with similar documents by using `similarity_search_with_score`: - chroma - docarray_hnsw - docarray_in_memory - faiss - myscale - qdrant - supabase - vectara - weaviate However, in documents, these scores were either not explained at all or explained in a way that could lead to misunderstandings (e.g., FAISS). For instance in FAISS document: if we consider the score returned by the function as a similarity score, we understand that a document returning a higher score is more similar to the source document. However, since the scores returned by the function are distance scores, we should understand that smaller scores correspond to more similar documents. For the libraries other than Vectara, I wrote the scores they use by investigating from the source libraries. Since I couldn't be certain about the score metric used by Vectara, I didn't make any changes in its documentation. The links mentioned in Vectara's documentation became broken due to updates, so I replaced them with working ones. VectorStores / Retrievers / Memory - @dev2049 my twitter: [berkedilekoglu](https://twitter.com/berkedilekoglu) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
706 lines
22 KiB
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706 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "683953b3",
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"metadata": {},
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"source": [
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"# Qdrant\n",
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"\n",
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">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.\n",
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"\n",
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"\n",
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"This notebook shows how to use functionality related to the `Qdrant` vector database. \n",
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"\n",
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"There are various modes of how to run `Qdrant`, and depending on the chosen one, there will be some subtle differences. The options include:\n",
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"- Local mode, no server required\n",
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"- On-premise server deployment\n",
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"- Qdrant Cloud\n",
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"\n",
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"See the [installation instructions](https://qdrant.tech/documentation/install/)."
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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": "e03e8460-8f32-4d1f-bb93-4f7636a476fa",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install qdrant-client"
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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": "7b2f111b-357a-4f42-9730-ef0603bdc1b5",
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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": 2,
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"id": "082e7e8b-ac52-430c-98d6-8f0924457642",
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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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"OpenAI API Key: ········\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "aac9563e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:22.282884Z",
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"start_time": "2023-04-04T10:51:21.408077Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Qdrant\n",
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"from langchain.document_loaders import TextLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "a3c3999a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:22.520144Z",
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"start_time": "2023-04-04T10:51:22.285826Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"loader = TextLoader('../../../state_of_the_union.txt')\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "eeead681",
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"metadata": {},
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"source": [
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"## Connecting to Qdrant from LangChain\n",
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"\n",
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"### Local mode\n",
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"\n",
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"Python client allows you to run the same code in local mode without running the Qdrant server. That's great for testing things out and debugging or if you plan to store just a small amount of vectors. The embeddings might be fully kepy in memory or persisted on disk.\n",
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"\n",
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"#### In-memory\n",
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"\n",
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"For some testing scenarios and quick experiments, you may prefer to keep all the data in memory only, so it gets lost when the client is destroyed - usually at the end of your script/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": 5,
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"id": "8429667e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:22.525091Z",
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"start_time": "2023-04-04T10:51:22.522015Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"qdrant = Qdrant.from_documents(\n",
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" docs, embeddings, \n",
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" location=\":memory:\", # Local mode with in-memory storage only\n",
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" collection_name=\"my_documents\",\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": "59f0b954",
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"metadata": {},
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"source": [
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"#### On-disk storage\n",
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"\n",
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"Local mode, without using the Qdrant server, may also store your vectors on disk so they're persisted between runs."
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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": "24b370e2",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:24.827567Z",
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"start_time": "2023-04-04T10:51:22.529080Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"qdrant = Qdrant.from_documents(\n",
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" docs, embeddings, \n",
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" path=\"/tmp/local_qdrant\",\n",
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" collection_name=\"my_documents\",\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": "749658ce",
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"metadata": {},
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"source": [
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"### On-premise server deployment\n",
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"\n",
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"No matter if you choose to launch Qdrant locally with [a Docker container](https://qdrant.tech/documentation/install/), or select a Kubernetes deployment with [the official Helm chart](https://github.com/qdrant/qdrant-helm), the way you're going to connect to such an instance will be identical. You'll need to provide a URL pointing to the service."
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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": "91e7f5ce",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:24.832708Z",
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"start_time": "2023-04-04T10:51:24.829905Z"
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}
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},
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"outputs": [],
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"source": [
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"url = \"<---qdrant url here --->\"\n",
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"qdrant = Qdrant.from_documents(\n",
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" docs, embeddings, \n",
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" url, prefer_grpc=True, \n",
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" collection_name=\"my_documents\",\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": "c9e21ce9",
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"metadata": {},
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"source": [
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"### Qdrant Cloud\n",
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"\n",
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"If you prefer not to keep yourself busy with managing the infrastructure, you can choose to set up a fully-managed Qdrant cluster on [Qdrant Cloud](https://cloud.qdrant.io/). There is a free forever 1GB cluster included for trying out. The main difference with using a managed version of Qdrant is that you'll need to provide an API key to secure your deployment from being accessed publicly."
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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": "dcf88bdf",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:24.837599Z",
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"start_time": "2023-04-04T10:51:24.834690Z"
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}
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},
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"outputs": [],
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"source": [
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"url = \"<---qdrant cloud cluster url here --->\"\n",
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"api_key = \"<---api key here--->\"\n",
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"qdrant = Qdrant.from_documents(\n",
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" docs, embeddings, \n",
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" url, prefer_grpc=True, api_key=api_key, \n",
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" collection_name=\"my_documents\",\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": "93540013",
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"metadata": {},
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"source": [
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"## Reusing the same collection\n",
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"\n",
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"Both `Qdrant.from_texts` and `Qdrant.from_documents` methods are great to start using Qdrant with LangChain, but **they are going to destroy the collection and create it from scratch**! If you want to reuse the existing collection, you can always create an instance of `Qdrant` on your own and pass the `QdrantClient` instance with the connection details."
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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": "b7b432d7",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:24.843090Z",
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"start_time": "2023-04-04T10:51:24.840041Z"
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}
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},
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"outputs": [],
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"source": [
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"del qdrant"
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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": "30a87570",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:24.854117Z",
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"start_time": "2023-04-04T10:51:24.845385Z"
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}
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},
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"outputs": [],
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"source": [
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"import qdrant_client\n",
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"\n",
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"client = qdrant_client.QdrantClient(\n",
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" path=\"/tmp/local_qdrant\", prefer_grpc=True\n",
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")\n",
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"qdrant = Qdrant(\n",
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" client=client, collection_name=\"my_documents\", \n",
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" embeddings=embeddings\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": "1f9215c8",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T09:27:29.920258Z",
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"start_time": "2023-04-04T09:27:29.913714Z"
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}
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},
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"source": [
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"## Similarity search\n",
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"\n",
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"The simplest scenario for using Qdrant vector store is to perform a similarity search. Under the hood, our query will be encoded with the `embedding_function` and used to find similar documents in Qdrant collection."
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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": "a8c513ab",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.204469Z",
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"start_time": "2023-04-04T10:51:24.855618Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"found_docs = qdrant.similarity_search(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "fc516993",
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"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2023-04-04T10:51:25.220984Z",
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"start_time": "2023-04-04T10:51:25.213943Z"
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},
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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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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
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]
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}
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],
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"source": [
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"print(found_docs[0].page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "1bda9bf5",
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"metadata": {},
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"source": [
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"## Similarity search with score\n",
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"\n",
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"Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result. \n",
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"The returned distance score is cosine distance. Therefore, a lower score is better."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "8804a21d",
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"metadata": {
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"ExecuteTime": {
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||
"end_time": "2023-04-04T10:51:25.631585Z",
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"start_time": "2023-04-04T10:51:25.227384Z"
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}
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},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"found_docs = qdrant.similarity_search_with_score(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "756a6887",
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||
"metadata": {
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||
"ExecuteTime": {
|
||
"end_time": "2023-04-04T10:51:25.642282Z",
|
||
"start_time": "2023-04-04T10:51:25.635947Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
|
||
"text": [
|
||
"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
|
||
"\n",
|
||
"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
|
||
"\n",
|
||
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
|
||
"\n",
|
||
"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
|
||
"\n",
|
||
"Score: 0.8153784913324512\n"
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||
]
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||
}
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||
],
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"source": [
|
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"document, score = found_docs[0]\n",
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"print(document.page_content)\n",
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"print(f\"\\nScore: {score}\")"
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]
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||
},
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||
{
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||
"attachments": {},
|
||
"cell_type": "markdown",
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||
"id": "525e3582",
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||
"metadata": {},
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"source": [
|
||
"### Metadata filtering\n",
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"\n",
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||
"Qdrant has an [extensive filtering system](https://qdrant.tech/documentation/concepts/filtering/) with rich type support. It is also possible to use the filters in Langchain, by passing an additional param to both the `similarity_search_with_score` and `similarity_search` methods."
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||
]
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||
},
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||
{
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||
"attachments": {},
|
||
"cell_type": "markdown",
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||
"id": "1c2c58dc",
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||
"metadata": {},
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||
"source": [
|
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"```python\n",
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"from qdrant_client.http import models as rest\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"found_docs = qdrant.similarity_search_with_score(query, filter=rest.Filter(...))\n",
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"```"
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||
]
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},
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||
{
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||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"id": "c58c30bf",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-04-04T10:39:53.032744Z",
|
||
"start_time": "2023-04-04T10:39:53.028673Z"
|
||
}
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||
},
|
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"source": [
|
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"## Maximum marginal relevance search (MMR)\n",
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"\n",
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"If you'd like to look up for some similar documents, but you'd also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents."
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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": "76810fb6",
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||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-04-04T10:51:26.010947Z",
|
||
"start_time": "2023-04-04T10:51:25.647687Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
|
||
"found_docs = qdrant.max_marginal_relevance_search(query, k=2, fetch_k=10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "80c6db11",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-04-04T10:51:26.016979Z",
|
||
"start_time": "2023-04-04T10:51:26.013329Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"1. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
|
||
"\n",
|
||
"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
|
||
"\n",
|
||
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
|
||
"\n",
|
||
"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
|
||
"\n",
|
||
"2. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n",
|
||
"\n",
|
||
"I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n",
|
||
"\n",
|
||
"They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n",
|
||
"\n",
|
||
"Officer Mora was 27 years old. \n",
|
||
"\n",
|
||
"Officer Rivera was 22. \n",
|
||
"\n",
|
||
"Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n",
|
||
"\n",
|
||
"I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n",
|
||
"\n",
|
||
"I’ve worked on these issues a long time. \n",
|
||
"\n",
|
||
"I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. \n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for i, doc in enumerate(found_docs):\n",
|
||
" print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"id": "691a82d6",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Qdrant as a Retriever\n",
|
||
"\n",
|
||
"Qdrant, as all the other vector stores, is a LangChain Retriever, by using cosine similarity. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "9427195f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-04-04T10:51:26.031451Z",
|
||
"start_time": "2023-04-04T10:51:26.018763Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='similarity', search_kwargs={})"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"retriever = qdrant.as_retriever()\n",
|
||
"retriever"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"id": "0c851b4f",
|
||
"metadata": {},
|
||
"source": [
|
||
"It might be also specified to use MMR as a search strategy, instead of similarity."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "64348f1b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-04-04T10:51:26.043909Z",
|
||
"start_time": "2023-04-04T10:51:26.034284Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='mmr', search_kwargs={})"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"retriever = qdrant.as_retriever(search_type=\"mmr\")\n",
|
||
"retriever"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "f3c70c31",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-04-04T10:51:26.495652Z",
|
||
"start_time": "2023-04-04T10:51:26.046407Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
|
||
"retriever.get_relevant_documents(query)[0]"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"id": "0358ecde",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Customizing Qdrant\n",
|
||
"\n",
|
||
"Qdrant stores your vector embeddings along with the optional JSON-like payload. Payloads are optional, but since LangChain assumes the embeddings are generated from the documents, we keep the context data, so you can extract the original texts as well.\n",
|
||
"\n",
|
||
"By default, your document is going to be stored in the following payload structure:\n",
|
||
"\n",
|
||
"```json\n",
|
||
"{\n",
|
||
" \"page_content\": \"Lorem ipsum dolor sit amet\",\n",
|
||
" \"metadata\": {\n",
|
||
" \"foo\": \"bar\"\n",
|
||
" }\n",
|
||
"}\n",
|
||
"```\n",
|
||
"\n",
|
||
"You can, however, decide to use different keys for the page content and metadata. That's useful if you already have a collection that you'd like to reuse. You can always change the "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "e4d6baf9",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-04-04T11:08:31.739141Z",
|
||
"start_time": "2023-04-04T11:08:30.229748Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<langchain.vectorstores.qdrant.Qdrant at 0x7fc4e2baa230>"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"Qdrant.from_documents(\n",
|
||
" docs, embeddings, \n",
|
||
" location=\":memory:\",\n",
|
||
" collection_name=\"my_documents_2\",\n",
|
||
" content_payload_key=\"my_page_content_key\",\n",
|
||
" metadata_payload_key=\"my_meta\",\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "2300e785",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"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": "3.11.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|