mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-28 10:39:23 +00:00
docs integrations/embeddings consistency (#10302)
Updated `integrations/embeddings`: fixed titles; added links, descriptions Updated `integrations/providers`.
This commit is contained in:
Leonid Ganeline
GitHub
parent
1b3ea1eeb4
commit
fdba711d28
@ -2216,6 +2216,10 @@
|
||||
"source": "/docs/modules/data_connection/text_embedding/integrations/tensorflowhub",
|
||||
"destination": "/docs/integrations/text_embedding/tensorflowhub"
|
||||
},
|
||||
{
|
||||
"source": "/docs/integrations/text_embedding/Awa",
|
||||
"destination": "/docs/integrations/text_embedding/awadb"
|
||||
},
|
||||
{
|
||||
"source": "/en/latest/modules/indexes/vectorstores/examples/analyticdb.html",
|
||||
"destination": "/docs/integrations/vectorstores/analyticdb"
|
||||
|
||||
@ -9,13 +9,20 @@ pip install awadb
|
||||
```
|
||||
|
||||
|
||||
## VectorStore
|
||||
## Vector Store
|
||||
|
||||
There exists a wrapper around AwaDB vector databases, allowing you to use it as a vectorstore,
|
||||
whether for semantic search or example selection.
|
||||
|
||||
```python
|
||||
from langchain.vectorstores import AwaDB
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of the AwaDB wrapper, see [here](/docs/integrations/vectorstores/awadb.html).
|
||||
See a [usage example](/docs/integrations/vectorstores/awadb).
|
||||
|
||||
|
||||
## Text Embedding Model
|
||||
|
||||
```python
|
||||
from langchain.embeddings import AwaEmbeddings
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/text_embedding/awadb).
|
||||
|
||||
@ -1,20 +1,24 @@
|
||||
# ModelScope
|
||||
|
||||
>[ModelScope](https://www.modelscope.cn/home) is a big repository of the models and datasets.
|
||||
|
||||
This page covers how to use the modelscope ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
* Install the Python SDK with `pip install modelscope`
|
||||
Install the `modelscope` package.
|
||||
|
||||
```bash
|
||||
pip install modelscope
|
||||
```
|
||||
|
||||
## Wrappers
|
||||
|
||||
### Embeddings
|
||||
## Text Embedding Models
|
||||
|
||||
There exists a modelscope Embeddings wrapper, which you can access with
|
||||
|
||||
```python
|
||||
from langchain.embeddings import ModelScopeEmbeddings
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/modelscope_hub.html)
|
||||
For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/modelscope_hub)
|
||||
|
||||
@ -1,17 +1,31 @@
|
||||
# NLPCloud
|
||||
|
||||
This page covers how to use the NLPCloud ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers.
|
||||
>[NLP Cloud](https://docs.nlpcloud.com/#introduction) is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data.
|
||||
|
||||
|
||||
## Installation and Setup
|
||||
- Install the Python SDK with `pip install nlpcloud`
|
||||
|
||||
- Install the `nlpcloud` package.
|
||||
|
||||
```bash
|
||||
pip install nlpcloud
|
||||
```
|
||||
|
||||
- Get an NLPCloud api key and set it as an environment variable (`NLPCLOUD_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
## LLM
|
||||
|
||||
See a [usage example](/docs/integrations/llms/nlpcloud).
|
||||
|
||||
There exists an NLPCloud LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import NLPCloud
|
||||
```
|
||||
|
||||
## Text Embedding Models
|
||||
|
||||
See a [usage example](/docs/integrations/text_embedding/nlp_cloud)
|
||||
|
||||
```python
|
||||
from langchain.embeddings import NLPCloudEmbeddings
|
||||
```
|
||||
|
||||
@ -18,3 +18,11 @@ See a [usage example](/docs/modules/data_connection/document_transformers/text_s
|
||||
```python
|
||||
from langchain.text_splitter import SpacyTextSplitter
|
||||
```
|
||||
|
||||
## Text Embedding Models
|
||||
|
||||
See a [usage example](/docs/integrations/text_embedding/spacy_embedding)
|
||||
|
||||
```python
|
||||
from langchain.embeddings.spacy_embeddings import SpacyEmbeddings
|
||||
```
|
||||
|
||||
@ -5,9 +5,11 @@
|
||||
"id": "b14a24db",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# AwaEmbedding\n",
|
||||
"# AwaDB\n",
|
||||
"\n",
|
||||
"This notebook explains how to use AwaEmbedding, which is included in [awadb](https://github.com/awa-ai/awadb), to embedding texts in langchain."
|
||||
">[AwaDB](https://github.com/awa-ai/awadb) is an AI Native database for the search and storage of embedding vectors used by LLM Applications.\n",
|
||||
"\n",
|
||||
"This notebook explains how to use `AwaEmbeddings` in LangChain."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -101,7 +103,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
@ -5,7 +5,9 @@
|
||||
"id": "75e378f5-55d7-44b6-8e2e-6d7b8b171ec4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bedrock Embeddings"
|
||||
"# Bedrock\n",
|
||||
"\n",
|
||||
">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -91,7 +93,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.13"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -5,26 +5,29 @@
|
||||
"id": "719619d3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# BGE Hugging Face Embeddings\n",
|
||||
"# BGE on Hugging Face\n",
|
||||
"\n",
|
||||
"This notebook shows how to use BGE Embeddings through Hugging Face"
|
||||
">[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).\n",
|
||||
">BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://www.baai.ac.cn/english.html). `BAAI` is a private non-profit organization engaged in AI research and development.\n",
|
||||
"\n",
|
||||
"This notebook shows how to use `BGE Embeddings` through `Hugging Face`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": null,
|
||||
"id": "f7a54279",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# !pip install sentence_transformers"
|
||||
"#!pip install sentence_transformers"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": null,
|
||||
"id": "9e1d5b6b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -43,12 +46,24 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 5,
|
||||
"id": "e59d1a89",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"384"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"embedding = hf.embed_query(\"hi this is harrison\")"
|
||||
"embedding = hf.embed_query(\"hi this is harrison\")\n",
|
||||
"len(embedding)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -76,7 +91,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -1,13 +1,14 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Google Cloud Platform Vertex AI PaLM \n",
|
||||
"# Google Vertex AI PaLM \n",
|
||||
"\n",
|
||||
"Note: This is seperate from the Google PaLM integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on Google Cloud. \n",
|
||||
">[Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) is a service on Google Cloud exposing the embedding models. \n",
|
||||
"\n",
|
||||
"Note: This integration is seperate from the Google PaLM integration.\n",
|
||||
"\n",
|
||||
"By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
|
||||
"\n",
|
||||
@ -96,7 +97,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -1,12 +1,13 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# ModelScope\n",
|
||||
"\n",
|
||||
">[ModelScope](https://www.modelscope.cn/home) is big repository of the models and datasets.\n",
|
||||
"\n",
|
||||
"Let's load the ModelScope Embedding class."
|
||||
]
|
||||
},
|
||||
@ -67,16 +68,23 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "chatgpt",
|
||||
"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",
|
||||
"version": "3.9.15"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -1,15 +1,14 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MosaicML embeddings\n",
|
||||
"# MosaicML\n",
|
||||
"\n",
|
||||
"[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
|
||||
">[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
|
||||
"\n",
|
||||
"This example goes over how to use LangChain to interact with MosaicML Inference for text embedding."
|
||||
"This example goes over how to use LangChain to interact with `MosaicML` Inference for text embedding."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -94,6 +93,11 @@
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
@ -103,9 +107,10 @@
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3"
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
"source": [
|
||||
"# NLP Cloud\n",
|
||||
"\n",
|
||||
"NLP Cloud is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. \n",
|
||||
">[NLP Cloud](https://docs.nlpcloud.com/#introduction) is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. \n",
|
||||
"\n",
|
||||
"The [embeddings](https://docs.nlpcloud.com/#embeddings) endpoint offers the following model:\n",
|
||||
"\n",
|
||||
@ -80,7 +80,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.11.2 64-bit",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -94,7 +94,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -5,11 +5,13 @@
|
||||
"id": "1f83f273",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# SageMaker Endpoint Embeddings\n",
|
||||
"# SageMaker\n",
|
||||
"\n",
|
||||
"Let's load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.\n",
|
||||
"Let's load the `SageMaker Endpoints Embeddings` class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.\n",
|
||||
"\n",
|
||||
"For instructions on how to do this, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker). **Note**: In order to handle batched requests, you will need to adjust the return line in the `predict_fn()` function within the custom `inference.py` script:\n",
|
||||
"For instructions on how to do this, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker). \n",
|
||||
"\n",
|
||||
"**Note**: In order to handle batched requests, you will need to adjust the return line in the `predict_fn()` function within the custom `inference.py` script:\n",
|
||||
"\n",
|
||||
"Change from\n",
|
||||
"\n",
|
||||
@ -143,7 +145,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -5,8 +5,8 @@
|
||||
"id": "eec4efda",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Self Hosted Embeddings\n",
|
||||
"Let's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes."
|
||||
"# Self Hosted\n",
|
||||
"Let's load the `SelfHostedEmbeddings`, `SelfHostedHuggingFaceEmbeddings`, and `SelfHostedHuggingFaceInstructEmbeddings` classes."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -149,9 +149,7 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fc1bfd0f",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(text)"
|
||||
@ -182,7 +180,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -1,16 +1,15 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"id": "ed47bb62",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Sentence Transformers Embeddings\n",
|
||||
"# Sentence Transformers\n",
|
||||
"\n",
|
||||
"[SentenceTransformers](https://www.sbert.net/) embeddings are called using the `HuggingFaceEmbeddings` integration. We have also added an alias for `SentenceTransformerEmbeddings` for users who are more familiar with directly using that package.\n",
|
||||
">[SentenceTransformers](https://www.sbert.net/) embeddings are called using the `HuggingFaceEmbeddings` integration. We have also added an alias for `SentenceTransformerEmbeddings` for users who are more familiar with directly using that package.\n",
|
||||
"\n",
|
||||
"SentenceTransformers is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
|
||||
"`SentenceTransformers` is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -109,7 +108,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.16"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -1,21 +1,31 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Spacy Embedding\n",
|
||||
"# SpaCy\n",
|
||||
"\n",
|
||||
"### Loading the Spacy embedding class to generate and query embeddings"
|
||||
">[spaCy](https://spacy.io/) is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"## Installation and Setup"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install spacy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Import the necessary classes"
|
||||
"Import the necessary classes"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -28,11 +38,12 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Initialize SpacyEmbeddings.This will load the Spacy model into memory."
|
||||
"## Example\n",
|
||||
"\n",
|
||||
"Initialize SpacyEmbeddings.This will load the Spacy model into memory."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -45,11 +56,10 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews."
|
||||
"Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -67,11 +77,10 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Generate and print embeddings for the texts . The SpacyEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification."
|
||||
"Generate and print embeddings for the texts . The SpacyEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -86,11 +95,10 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query."
|
||||
"Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -106,11 +114,24 @@
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user