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{
"cells": [
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts.few_shot import FewShotPromptTemplate\n",
"from langchain.prompts.prompt import PromptTemplate\n",
"from langchain.prompts.example_selector import SemanticSimilarityExampleSelector\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.llms import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"examples = [\n",
" {\"question\":\"天气怎么样\", \"answer\":\"晴\"},\n",
" {\"question\":\"北京天气怎么样\", \"answer\":\"加法\"},\n",
" {\"question\":\"咋样\", \"answer\":\"加法\"},\n",
" {\"question\":\"天儿咋样\", \"answer\":\"加法\"},\n",
" {\"question\":\"今天有雾\", \"answer\":\"加法\"},\n",
" {\"question\":\"吃得怎么样\", \"answer\":\"加法\"},\n",
" {\"question\":\"你叫什么\", \"answer\":\"加法\"},\n",
" {\"question\":\"你叫嘿嘿\", \"answer\":\"加法\"},\n",
" {\"question\":\"是吗\", \"answer\":\"加法\"},\n",
" {\"question\":\"10-1=9\", \"answer\":\"作业帮回答：减法\"},\n",
" {\"question\":\"9-1=8\", \"answer\":\"作业帮回答：减法\"},\n",
" {\"question\":\"8-1=7\", \"answer\":\"作业帮回答：减法\"},\n",
" {\"question\":\"7-1=6\", \"answer\":\"作业帮回答：减法\"},\n",
" {\"question\":\"7-2=5\", \"answer\":\"作业帮回答：减法\"},\n",
" {\"question\":\"10-3=7\", \"answer\":\"作业帮回答：减法\"},\n",
" {\"question\":\"10-4=6\", \"answer\":\"作业帮回答：减法\"},\n",
" {\"question\":\"10-5=5\", \"answer\":\"作业帮回答：减法\"},\n",
" {\"question\":\"10+5=15\", \"answer\":\"作业帮回答：加法\"},\n",
" {\"question\":\"10*5=50\", \"answer\":\"作业帮回答：乘法\"},\n",
" {\"question\":\"10/5=2\", \"answer\":\"作业帮回答：除法\"},\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using embedded DuckDB without persistence: data will be transient\n"
]
}
],
"source": [
"example_selector = SemanticSimilarityExampleSelector.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",
" Chroma,\n",
" # This is the number of examples to produce.\n",
" k=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"example_prompt = PromptTemplate(input_variables=[\"question\", \"answer\"], template=\"Question: {question}\\n{answer}\")"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Question: 10+5=15\n",
"作业帮回答：加法\n",
"\n",
"Question: 1+2=？\n"
]
}
],
"source": [
"prompt = FewShotPromptTemplate(\n",
" example_selector=example_selector, \n",
" example_prompt=example_prompt, \n",
" suffix=\"Question: {input}\", \n",
" input_variables=[\"input\"]\n",
")\n",
"\n",
"print(prompt.format(input=\"1+2=？\"))"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'？\\n很晴朗，非常适合出去活动。'"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm = OpenAI(temperature=0)\n",
"llm(prompt.format(input=\"天气怎么样\"))"
]
}
],
"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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