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https://github.com/hwchase17/langchain.git
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87e502c6bc
Co-authored-by: jacoblee93 <jacoblee93@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
134 lines
3.2 KiB
Plaintext
134 lines
3.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "872bb8b5",
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"metadata": {},
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"source": [
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"# Transformation\n",
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"\n",
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"This notebook showcases using a generic transformation chain.\n",
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"\n",
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"As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an LLMChain to summarize those."
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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": "bbbb4330",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import TransformChain, LLMChain, SimpleSequentialChain\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate"
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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": "8ae5937c",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"../../state_of_the_union.txt\") as f:\n",
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" state_of_the_union = f.read()"
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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": 4,
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"id": "98739592",
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"metadata": {},
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"outputs": [],
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"source": [
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"def transform_func(inputs: dict) -> dict:\n",
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" text = inputs[\"text\"]\n",
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" shortened_text = \"\\n\\n\".join(text.split(\"\\n\\n\")[:3])\n",
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" return {\"output_text\": shortened_text}\n",
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"\n",
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"\n",
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"transform_chain = TransformChain(\n",
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" input_variables=[\"text\"], output_variables=[\"output_text\"], transform=transform_func\n",
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")"
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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": "e9397934",
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Summarize this text:\n",
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"\n",
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"{output_text}\n",
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"\n",
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"Summary:\"\"\"\n",
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"prompt = PromptTemplate(input_variables=[\"output_text\"], template=template)\n",
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"llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)"
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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": "06f51f17",
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"metadata": {},
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"outputs": [],
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"source": [
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"sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])"
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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": 7,
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"id": "f7caa1ee",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sequential_chain.run(state_of_the_union)"
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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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"id": "e3ca6409",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.11.3"
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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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