mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
117 lines
3.0 KiB
Plaintext
117 lines
3.0 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Spacy Embedding\n",
|
|
"\n",
|
|
"### Loading the Spacy embedding class to generate and query embeddings"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Import the necessary classes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.embeddings.spacy_embeddings import SpacyEmbeddings"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Initialize SpacyEmbeddings.This will load the Spacy model into memory."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"embedder = SpacyEmbeddings()"
|
|
]
|
|
},
|
|
{
|
|
"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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"texts = [\n",
|
|
" \"The quick brown fox jumps over the lazy dog.\",\n",
|
|
" \"Pack my box with five dozen liquor jugs.\",\n",
|
|
" \"How vexingly quick daft zebras jump!\",\n",
|
|
" \"Bright vixens jump; dozy fowl quack.\",\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"embeddings = embedder.embed_documents(texts)\n",
|
|
"for i, embedding in enumerate(embeddings):\n",
|
|
" print(f\"Embedding for document {i+1}: {embedding}\")"
|
|
]
|
|
},
|
|
{
|
|
"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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"query = \"Quick foxes and lazy dogs.\"\n",
|
|
"query_embedding = embedder.embed_query(query)\n",
|
|
"print(f\"Embedding for query: {query_embedding}\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"language_info": {
|
|
"name": "python"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|