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81 lines
1.5 KiB
Plaintext
81 lines
1.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f9c02c78",
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"metadata": {},
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"source": [
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"# Fake Embeddings\n",
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"\n",
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"LangChain also provides a fake embedding class. You can use this to test your pipelines."
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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": 1,
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"id": "2ffc2e4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import FakeEmbeddings"
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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": "80777571",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = FakeEmbeddings(size=1352)"
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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": "3ec9d8f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(\"foo\")"
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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": "3b9ae9e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_results = embeddings.embed_documents([\"foo\"])"
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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": "3.9.1"
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},
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"vscode": {
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"interpreter": {
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"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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