程式環境都會用colab 來執行程式，如果要在其他環境執行，請自行修改哦
colab 事前準備：設定專案和 API 金鑰
載入gemini
#pip install -q -U google-generativeai
import google.generativeai as genai
API 金鑰
from google.colab import userdata
API_KEY=userdata.get('GOOGLE_API_KEY')
#genai.configure(api_key="YOUR_API_KEY")
# Configure the client library by providing your API key.
genai.configure(api_key=API_KEY)
import textwrap
import numpy as np
import pandas as pd
from IPython.display import Markdown
for m in genai.list_models():
if 'embedContent' in m.supported_generation_methods:
print(m.name)
回答
models/embedding-001
models/text-embedding-004
使用 Gemini API 建立嵌入項目，以便執行文件搜尋。
使用嵌入功能在一組文件中執行文件搜尋，以詢問與 Google Car 有關的問題
工作類型如下：
title = "The next generation of AI for developers and Google Workspace"
sample_text = ("Title: The next generation of AI for developers and Google Workspace"
"\n"
"Full article:\n"
"\n"
"Gemini API & Google AI Studio: An approachable way to explore and prototype with generative AI applications")
model = 'models/embedding-001'
embedding = genai.embed_content(model=model,
content=sample_text,
task_type="retrieval_document",
title=title)
print(embedding)
回答
{'embedding': [0.03411343, -0.05517662, -0.020209055, -0.0041249567, 0.058917783, 0.014129515, 0.0045353593, 0.0014303668, 0.05976634, 0.08292115, 0.007162964, 0.0069041685, -0.053083427, -0.010905125, 0.0321402, -0.037163995, 0.050372455, 0.019348344, -0.037328612, 0.026647927, 0.030781753, -0.011288501, -0.031485256, -0.060248993, -0.026219442, -0.009794235, 0.006630139, -0.01846516, -0.026324715, 0.020442624, -0.06317684, 0.014559574, -0.052296035, 0.016451128, -9.720217e-05, -0.051706687, -0.0054406044, -0.056967627, 0.011144145, -0.009201792, -0.0021951047, -0.1099701, -0.011712193, 0.021221714, 0.009171804, -0.029621972, 0.034534883, 0.039578073, 0.019021519, -0.06269169, 0.039473332, 0.052403256, 0.061814185, -0.034507945, -0.009557816, -0.0049551064, 0.017839009, -0.021176832, 0.015043588, 0.015390569, -0.006334281, 0.043696404, -0.028341983, 0.028433999, 0.01472686, -0.06585564, -0.044533554, 0.0055523133, 0.035775978, 0.031099156, 0.027357662, 0.028062241, 0.056972917, -0.054656833, -0.027864764, -0.15486294, -0.027930057, 0.043678433, 0.008391214, 0.020209847, 0.002841071, -0.07201404, -0.05025868, -0.034896467, -0.030400582, 0.016623711, -0.050455835, -0.025557702, 0.0050540236, 0.032266915, -0.018223321, -0.04913693, 0.07667526, -0.03066128, -0.0127946865, 0.107169494, -0.0563475, -0.016773727, -0.010336115, -0.05220995, -0.022049127, 0.00478732, -0.039094422, 0.015671168, 0.041542538, 0.016112784, -0.022650082, 0.002988097, -0.061147556, 0.06630078, -0.057244215, -0.013767544, -0.003466806, -0.053994596, 0.04230463, -0.029314812, 0.021347178, 0.04522084, 0.0072983643, 0.0247336, 0.020755325, 0.025620919, 0.021721177, 0.008178421, -0.063603185, 0.025854606, -0.037521806, 0.020877894, 0.033131972, 0.030288944, 0.0033810628, -0.048698667, -0.027295412, 0.05622969, 0.029634966, 0.029705161, 0.010602056, -0.02217137, 0.03195607, 0.047208548, -0.013620148, 0.038463745, 0.0052672145, -0.024868716, -0.0071725682, 0.069668904, -0.092124775, 0.014151632, 0.0057337824, -0.006012909, -0.035946254, 0.0334855, -0.07426327, 0.033595767, 0.033740804, 0.0394573, -0.048899952, 0.06265119, 0.0028897377, 0.0039847936, 0.0329678, -0.012809373, 0.050108086, 0.009314281, -0.019499086, -0.048860364, -0.015204039, 0.008016007, 0.015667893, 0.03903864, 0.011732032, 0.034669068, -0.01226258, -0.052623957, 0.006695629, -0.05849198, 0.00101155, -0.009621011, -0.0052014636, -0.020959012, -0.02466722, -0.03861565, 0.049754824, 0.048655674, 0.0044479654, -0.020404046, 0.101043485, -0.022594253, -0.06822699, 0.044780556, -0.03859346, -0.015194885, -0.0059435116, -0.016267126, 0.0012126336, 0.054198146, 0.01978253, 0.02905382, 0.034172967, -0.0032252679, 0.020003818, 0.07212547, 0.035888623, -0.00029856138, 0.0044168616, 0.036989234, 0.100975856, 0.0048228516, -0.04405796, 0.00039434276, -0.044601627, -0.011658614, 0.03398768, 0.02250937, 0.034583274, -0.03440395, -0.003274625, -0.005927225, -0.007679341, -0.025777208, -0.02205426, 0.00823437, -0.027172998, 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0.072175056, 0.038597208, -0.038364347, -0.005039459, -0.07634857, -0.045539834, -0.07372115, 0.018378831, -0.032071035, -0.030269828, -0.044599615, 0.05132602, 0.04769642, 0.0014855171, -0.028435005, -0.0016777773, 0.0072506564, 0.08448479, 0.04224268, -0.029304115, 0.02165559, 0.0056837583, 0.06214723, 0.0028552667, 0.015904495, -0.016737062, -0.0040876116, -0.037475582, 0.04675168, -0.052556172, -0.016293239, -0.014435561, 0.022127734, -0.0052535324, 0.0190588, -0.011537659, 0.0484614, 0.028816467, 0.024607794, -0.043762755, 0.011608192, 0.0021703655, -0.045297306, -0.0048169023, -0.0071430723, 0.011705694, -0.05006429, 0.029933244, -0.020802287, -0.07580785, -0.012268235, 0.07616304, 0.006857885, 0.01853518, 0.043729052, -0.032221675, -0.010121849, -0.019831154, -0.032731093, 0.051531196, 0.0024111927, 0.090960525, -0.036896333, 0.035708647, 0.03678696, 0.00832481, 0.001757778, 0.04144341, 0.042203393, 0.0045936033, 0.021837635, 0.0066275136, 0.0069022025, -0.008452904, -0.03277543, 0.0061044246, -0.02500629, 0.012441071, 0.018081166, -0.06230864, -0.040046707, -0.019351328, 0.0007255696, 0.002202931, -0.041990966, 0.023313772, 0.039377946, -0.0012839311, 0.010378518, 0.0025737497, 0.043841247, -0.0067742136, 0.045794934, 0.01388272, 0.032243907, 0.0919292, 0.03760722, 0.0060486114, 0.010843367, 0.001991803, -0.04838942, -0.006412631, -0.030764624, -0.015602797, -0.048885867, -0.015245706, -0.0006477355, 0.013608845, 0.0040335134, -0.0015530499, -0.008402027, -0.05728556, -0.027370622, 0.019342335, -0.039145477, 0.049000833, -0.052876346, -0.060248777, 0.009484413, 0.011271402, -0.0019944878, -0.013369263, -0.0130786, 0.0050903647, -0.0003995775, 0.04580157, -0.030488051, -0.07777237, 0.022998745, -0.007693635, -0.013473893, 0.0071830116, 0.014312745, 0.019949466, 0.034036275, -0.0011623668, 0.022655929, -0.0049825236, -0.036455333, 0.0033899196, 0.020583669, -0.010457001, 0.027299065, 0.034606297, -0.0111719165, -0.013660416, -0.02705466, 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0.020671992, 0.068610825, 0.018303277, -0.08433639, 0.0023544622, -0.009237108, -0.0410166, 0.012912618, -0.035220295, 0.032994937, -0.0063333404, -0.028377546, 0.05429965, -0.022590995, -0.033762764, -0.0061482205, 0.0014308131, 0.05402618, -0.030298075, -0.020893354, 0.04020406, -0.013849863, -0.047842298, 0.032006662, 0.037729368, -0.02878951, 0.002758488, -0.0023380243, -0.052403864, 0.021707276, -0.02718091, 0.0045513017, 0.02493268, -0.016037108, 0.009521465, 0.022595555, -0.03332406, -0.01791281, -0.026219219, 0.015336862, 0.018615942, 0.0014700901, 0.005194217, -0.0059983027, -0.002134208, 0.055935774, 0.0002028429, -0.01381741, 0.0005677742, 0.052481145, -0.0056857914, -0.024219796, -0.0074823913, 0.041230515, 0.005571935, 0.06841099, -0.025634678, -0.037456885, -0.0021465495, -0.05163424, 0.048833348, 0.057269894, -0.0017718605, -0.012836743, 0.054180846, -0.032427873, -0.003244846, 0.01254491, 0.0071952185, 0.02080726, 0.015043071, -0.08000574, 0.047099367, -0.009071923, 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將資料集轉換為 DataFrame，提升圖表的呈現效果。
DOCUMENT1 = {
"title": "Operating the Climate Control System",
"content": "Your Googlecar has a climate control system that allows you to adjust the temperature and airflow in the car. To operate the climate control system, use the buttons and knobs located on the center console. Temperature: The temperature knob controls the temperature inside the car. Turn the knob clockwise to increase the temperature or counterclockwise to decrease the temperature. Airflow: The airflow knob controls the amount of airflow inside the car. Turn the knob clockwise to increase the airflow or counterclockwise to decrease the airflow. Fan speed: The fan speed knob controls the speed of the fan. Turn the knob clockwise to increase the fan speed or counterclockwise to decrease the fan speed. Mode: The mode button allows you to select the desired mode. The available modes are: Auto: The car will automatically adjust the temperature and airflow to maintain a comfortable level. Cool: The car will blow cool air into the car. Heat: The car will blow warm air into the car. Defrost: The car will blow warm air onto the windshield to defrost it."}
DOCUMENT2 = {
"title": "Touchscreen",
"content": "Your Googlecar has a large touchscreen display that provides access to a variety of features, including navigation, entertainment, and climate control. To use the touchscreen display, simply touch the desired icon. For example, you can touch the \"Navigation\" icon to get directions to your destination or touch the \"Music\" icon to play your favorite songs."}
DOCUMENT3 = {
"title": "Shifting Gears",
"content": "Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position. Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions."}
documents = [DOCUMENT1, DOCUMENT2, DOCUMENT3]
df = pd.DataFrame(documents)
df.columns = ['Title', 'Text']
df
回答
# Get the embeddings of each text and add to an embeddings column in the dataframe
def embed_fn(title, text):
return genai.embed_content(model=model,
content=text,
task_type="retrieval_document",
title=title)["embedding"]
df['Embeddings'] = df.apply(lambda row: embed_fn(row['Title'], row['Text']), axis=1)
df
回答
嵌入已產生，讓我們建立問答系統來搜尋這些文件。您將詢問超參數調整的相關問題、建立問題嵌入項目，並與 DataFrame 中的嵌入集合比較。
題目的嵌入會是向量 (浮點值清單)，並將採用內積的積積物與文件向量進行比較。API 傳回的這個向量已經過正規化。內積代表兩個向量之間的方向相似度。
工作類型
query = "How do you shift gears in the Google car?"
model = 'models/embedding-001'
request = genai.embed_content(model=model,
content=query,
task_type="retrieval_query")
def find_best_passage(query, dataframe):
"""
Compute the distances between the query and each document in the dataframe
using the dot product.
"""
query_embedding = genai.embed_content(model=model,
content=query,
task_type="retrieval_query")
dot_products = np.dot(np.stack(dataframe['Embeddings']), query_embedding["embedding"])
idx = np.argmax(dot_products)
return dataframe.iloc[idx]['Text'] # Return text from index with max value
passage = find_best_passage(query, df)
passage
回答
Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position. Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.
讓我們嘗試使用文字生成 API 建立 Q &一個系統。請在下方輸入您的自訂資料，建立簡單的問題和回答範例。你仍將以內心產品做為相似度指標。
def make_prompt(query, relevant_passage):
escaped = relevant_passage.replace("'", "").replace('"', "").replace("\n", " ")
prompt = textwrap.dedent("""You are a helpful and informative bot that answers questions using text from the reference passage included below. \
Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. \
However, you are talking to a non-technical audience, so be sure to break down complicated concepts and \
strike a friendly and converstional tone. \
If the passage is irrelevant to the answer, you may ignore it.
QUESTION: '{query}'
PASSAGE: '{relevant_passage}'
ANSWER:
""").format(query=query, relevant_passage=escaped)
return prompt
prompt = make_prompt(query, passage)
print(prompt)
回答
You are a helpful and informative bot that answers questions using text from the reference passage included below. Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. However, you are talking to a non-technical audience, so be sure to break down complicated concepts and strike a friendly and converstional tone. If the passage is irrelevant to the answer, you may ignore it.
QUESTION: 'How do you shift gears in the Google car?'
PASSAGE: 'Your Googlecar has an automatic transmission. To shift gears, simply move the shift lever to the desired position. Park: This position is used when you are parked. The wheels are locked and the car cannot move. Reverse: This position is used to back up. Neutral: This position is used when you are stopped at a light or in traffic. The car is not in gear and will not move unless you press the gas pedal. Drive: This position is used to drive forward. Low: This position is used for driving in snow or other slippery conditions.'
ANSWER:
for m in genai.list_models():
if 'generateContent' in m.supported_generation_methods:
print(m.name)
回答
models/gemini-1.0-pro-latest
models/gemini-1.0-pro
models/gemini-pro
models/gemini-1.0-pro-001
models/gemini-1.0-pro-vision-latest
models/gemini-pro-vision
models/gemini-1.5-pro-latest
models/gemini-1.5-pro-001
models/gemini-1.5-pro-002
models/gemini-1.5-pro
models/gemini-1.5-pro-exp-0801
models/gemini-1.5-pro-exp-0827
models/gemini-1.5-flash-latest
models/gemini-1.5-flash-001
models/gemini-1.5-flash-001-tuning
models/gemini-1.5-flash
models/gemini-1.5-flash-exp-0827
models/gemini-1.5-flash-8b-exp-0827
models/gemini-1.5-flash-8b-exp-0924
models/gemini-1.5-flash-002
model = genai.GenerativeModel('gemini-1.5-pro-latest')
answer = model.generate_content(prompt)
Markdown(answer.text)
回答
The Google car has an automatic transmission, so you don't have to worry about shifting gears manually. Just use the shift lever to choose between Park, Reverse, Neutral, and Drive, and the car will take care of the rest!
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