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fdba711d28
Updated `integrations/embeddings`: fixed titles; added links, descriptions Updated `integrations/providers`.
138 lines
3.4 KiB
Plaintext
138 lines
3.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# SpaCy\n",
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"\n",
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">[spaCy](https://spacy.io/) is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.\n",
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" \n",
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"\n",
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"## Installation and Setup"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install spacy"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Import the necessary classes"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings.spacy_embeddings import SpacyEmbeddings"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example\n",
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"\n",
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"Initialize SpacyEmbeddings.This will load the Spacy model into memory."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"embedder = SpacyEmbeddings()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"texts = [\n",
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" \"The quick brown fox jumps over the lazy dog.\",\n",
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" \"Pack my box with five dozen liquor jugs.\",\n",
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" \"How vexingly quick daft zebras jump!\",\n",
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" \"Bright vixens jump; dozy fowl quack.\",\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = embedder.embed_documents(texts)\n",
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"for i, embedding in enumerate(embeddings):\n",
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" print(f\"Embedding for document {i+1}: {embedding}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"Quick foxes and lazy dogs.\"\n",
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"query_embedding = embedder.embed_query(query)\n",
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"print(f\"Embedding for query: {query_embedding}\")"
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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": 4
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}
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