langchain/docs/extras/integrations/text_embedding/fake.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "f9c02c78",
"metadata": {},
"source": [
"# Fake Embeddings\n",
"\n",
"LangChain also provides a fake embedding class. You can use this to test your pipelines."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2ffc2e4b",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import FakeEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "80777571",
"metadata": {},
"outputs": [],
"source": [
"embeddings = FakeEmbeddings(size=1352)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3ec9d8f0",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(\"foo\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3b9ae9e1",
"metadata": {},
"outputs": [],
"source": [
"doc_results = embeddings.embed_documents([\"foo\"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"version": "3.9.1"
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