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{
"cells": [
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts.example_selector import MaxMarginalRelevanceExampleSelector\n",
"from langchain.vectorstores import FAISS\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
"from langchain.prompts.example_selector.ngram_overlap import NGramOverlapExampleSelector\n",
"\n",
"example_prompt = PromptTemplate(\n",
" input_variables=[\"input\", \"output\"],\n",
" template=\"Input: {input}\\nOutput: {output}\",\n",
")\n",
"\n",
"# These are a lot of examples of a pretend task of creating antonyms.\n",
"examples = [\n",
" {\"input\": \"高兴\", \"output\": \"悲伤\"},\n",
" {\"input\": \"个子高\", \"output\": \"个子矮\"},\n",
" {\"input\": \"精力充沛\", \"output\": \"昏昏欲睡\"},\n",
" {\"input\": \"晴朗\", \"output\": \"阴暗的阴暗的\"},\n",
" {\"input\": \"多风\", \"output\": \"平静的\"},\n",
" {\"input\": \"经济下滑一路到底\", \"output\": \"业绩增长\"},\n",
" {\"input\": \"飞翔\", \"output\": \"天空\"},\n",
" {\"input\": \"教育\", \"output\": \"平静的\"},\n",
" {\"input\": \"小孩儿\", \"output\": \"平静的\"},\n",
" {\"input\": \"开心\", \"output\": \"平静的\"},\n",
" {\"input\": \"消防员\", \"output\": \"平静的\"},\n",
" {\"input\": \"程序员\", \"output\": \"平静的\"},\n",
" {\"input\": \"理财师\", \"output\": \"平静的\"},\n",
" {\"input\": \"学生\", \"output\": \"平静的\"},\n",
" {\"input\": \"计算机\", \"output\": \"平静的\"},\n",
" {\"input\": \"See Spot run.\", \"output\": \"Ver correr a Spot.\"},\n",
" {\"input\": \"My dog barks.\", \"output\": \"Mi perro ladra.\"},\n",
" {\"input\": \"Spot can run.\", \"output\": \"Spot puede correr.\"},\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"example_selector = MaxMarginalRelevanceExampleSelector.from_examples(\n",
" # This is the list of examples available to select from.\n",
" examples, \n",
" # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n",
" OpenAIEmbeddings(), \n",
" # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n",
" FAISS, \n",
" # This is the number of examples to produce.\n",
" k=2\n",
")\n",
"mmr_prompt = FewShotPromptTemplate(\n",
" # We provide an ExampleSelector instead of examples.\n",
" example_selector=example_selector,\n",
" example_prompt=example_prompt,\n",
" prefix=\"Give the antonym of every input\",\n",
" suffix=\"Input: {adjective}\\nOutput:\", \n",
" input_variables=[\"adjective\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Give the antonym of every input\n",
"\n",
"Input: 理财师\n",
"Output: 平静的\n",
"\n",
"Input: See Spot run.\n",
"Output: Ver correr a Spot.\n",
"\n",
"Input: 投资\n",
"Output:\n"
]
}
],
"source": [
"# Input is a feeling, so should select the happy/sad example as the first one\n",
"print(mmr_prompt.format(adjective=\"投资\"))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"example_selector = NGramOverlapExampleSelector(\n",
" # These are the examples it has available to choose from.\n",
" examples=examples, \n",
" # This is the PromptTemplate being used to format the examples.\n",
" example_prompt=example_prompt, \n",
" # This is the threshold, at which selector stops.\n",
" # It is set to -1.0 by default.\n",
" threshold=-1.0,\n",
" # For negative threshold:\n",
" # Selector sorts examples by ngram overlap score, and excludes none.\n",
" # For threshold greater than 1.0:\n",
" # Selector excludes all examples, and returns an empty list.\n",
" # For threshold equal to 0.0:\n",
" # Selector sorts examples by ngram overlap score,\n",
" # and excludes those with no ngram overlap with input.\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"dynamic_prompt = FewShotPromptTemplate(\n",
" # We provide an ExampleSelector instead of examples.\n",
" example_selector=example_selector,\n",
" example_prompt=example_prompt,\n",
" prefix=\"Give the Spanish translation of every input\",\n",
" suffix=\"Input: {sentence}\\nOutput:\", \n",
" input_variables=[\"sentence\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Give the Spanish translation of every input\n",
"\n",
"Input: See Spot run.\n",
"Output: Ver correr a Spot.\n",
"\n",
"Input: Spot can run.\n",
"Output: Spot puede correr.\n",
"\n",
"Input: Spot run.\n",
"Output:\n"
]
}
],
"source": [
"# 没看懂有什么作用，0～1，\n",
"example_selector.threshold=0\n",
"print(dynamic_prompt.format(sentence=\" Spot run.\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.10"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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