 前往 ai.google.dev 查看
|
 在 Google Colab 中執行
|
 在 GitHub 上查看原始碼 |
總覽
本範例說明如何使用 Gemini API 建立嵌入，以便執行文件搜尋。您將使用 Python 用戶端程式庫建立字詞嵌入，這可讓您比較搜尋字串或問題來記錄內容。
在本教學課程中，您將使用嵌入功能在一組文件上執行文件搜尋，提出與 Google 街景車相關的問題。
必要條件
您可以在 Google Colab 中執行本快速入門導覽課程。
如要在自己的開發環境完成本快速入門導覽課程，請確保您的環境符合下列需求：
- Python 3.9 以上版本
- 安裝
jupyter以執行筆記本。
設定
首先，請下載並安裝 Gemini API Python 程式庫。
pip install -U -q google.generativeai
import textwrap
import numpy as np
import pandas as pd
import google.generativeai as genai
import google.ai.generativelanguage as glm
# Used to securely store your API key
from google.colab import userdata
from IPython.display import Markdown
取得 API 金鑰
您必須先取得 API 金鑰，才能使用 Gemini API。如果還沒有金鑰，請在 Google AI Studio 中按一下即可建立。
在 Colab 中，請在左側面板的「🔑?」下方，將密鑰新增至密鑰管理工具。輸入名稱 API_KEY。
取得 API 金鑰後，請傳遞至 SDK。操作方式有以下兩種：
- 將金鑰放入
GOOGLE_API_KEY環境變數中 (SDK 會自動從該處取得金鑰)。 - 將金鑰傳送至
genai.configure(api_key=...)
# Or use `os.getenv('API_KEY')` to fetch an environment variable.
API_KEY=userdata.get('API_KEY')
genai.configure(api_key=API_KEY)
for m in genai.list_models():
if 'embedContent' in m.supported_generation_methods:
print(m.name)
models/embedding-001 models/embedding-001
產生嵌入
本節將說明如何使用 Gemini API 的嵌入功能產生一段文字的嵌入。
使用模型嵌入功能進行嵌入式的 API 變更 -001
針對新的嵌入模型「embedding-001」，有一個新的工作類型參數和選填的標題 (僅適用於 task_type=RETRIEVAL_DOCUMENT)。
這些新參數僅適用於最新的嵌入模型。工作類型如下：
| 工作類型 | 說明 |
|---|---|
| RETRIEVAL_QUERY | 指出指定文字是搜尋/擷取設定中的查詢。 |
| RETRIEVAL_DOCUMENT | 指定指定文字是搜尋/擷取設定中的文件。 |
| SEMANTIC_SIMILARITY | 指定指定文字將用於語意文字相似度 (STS)。 |
| 分類 | 指定將嵌入用於分類。 |
| 叢集 | 指定會使用嵌入進行叢集處理。 |
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)
{'embedding': [0.034585103, -0.044509504, -0.027291223, 0.0072681927, 0.061689284, 0.03362112, 0.028627988, 0.022681564, 0.04958079, 0.07274552, 0.011150464, 0.04200501, -0.029782884, -0.0041767005, 0.05074771, -0.056339227, 0.051204756, 0.04734613, -0.022025354, 0.025162602, 0.046016376, -0.003416976, -0.024010269, -0.044340927, -0.01520864, -0.013577372, -0.009918958, -0.028144406, -0.00024770075, 0.031201784, -0.072506696, 0.022366496, -0.032672316, -0.0025522006, -0.0019957912, -0.023193765, -0.020633291, -0.014031609, -0.00071676675, -0.0073200124, 0.014770645, -0.09390713, -0.017846372, 0.032825496, 0.017616265, -0.046674345, 0.03469292, 0.03386835, 0.0028274113, -0.07737739, 0.023789782, 0.025950644, 0.06952142, -0.029875675, -0.018693604, 0.007266584, -0.0067282487, 0.000802912, 0.020609016, 0.012406181, -0.018825717, 0.051171597, -0.0080359895, 0.008457639, 0.01197146, -0.080320396, -0.040698495, 0.0018266322, 0.042915005, 0.021464704, 0.022519842, 0.0059912056, 0.050887667, -0.04566639, -0.012651369, -0.14023173, -0.0274054, 0.04492792, 0.014709818, 0.037258334, -0.021294944, -0.041852854, -0.069640376, -0.030281356, -0.0070775123, 0.019886682, -0.050179508, -0.03839318, -0.014652514, 0.03370254, -0.02803748, -0.059206057, 0.055928297, -0.034912255, -0.007784368, 0.098106734, -0.06873356, -0.052850258, -0.011798939, -0.030071719, -0.026038093, 0.016752971, -0.020916667, 0.007365556, 0.017650642, 0.006677715, -0.036498126, 0.02110524, -0.05625146, 0.043038886, -0.06515849, -0.019825866, -0.010379261, -0.037537806, 0.017674655, -0.042821705, 0.014320703, 0.036735073, 0.011445211, 0.027352763, -0.0028090556, 0.009011982, 0.024146665, 0.002215841, -0.07397819, 0.008714616, -0.03377923, 0.034349587, 0.022429721, 0.052665956, -0.0021583177, -0.040462274, -0.019938014, 0.030099798, 0.009743918, 0.009111553, 0.026379738, -0.015910586, 0.010171418, 0.023996552, -0.031924065, 0.024775924, 0.014129728, 0.008913726, -0.010156162, 0.05407575, -0.080851324, 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0.00057833333, -0.04805651, 0.01602842, -0.005916167, -0.0020399855, 0.036410075, -0.09505558, -0.021768136, 0.021421269, 0.024159726, -0.013026249, -0.023113504, 0.02459358, 0.01643742, -0.0104496805, 0.033115752, 0.047128692, 0.05519812, -0.013151745, 0.03202098, 0.0014973703, -0.009810199, 0.09950044, 0.03161514, 0.022533545, 0.028800217, 0.011425177, -0.06616128, 0.018490529, -0.024615118, -0.01714155, -0.036444064, -0.024078121, 6.236274e-05, -0.025733253, -0.012052791, -0.0032004463, -0.007022415, -0.07943268, -0.010401283, 0.014510383, -0.017218677, 0.056253612, -0.028017681, -0.06288073, -0.0010291388, 0.042233694, -0.017423663, -0.014384363, 0.008450004, -0.006025767, 0.00068278343, 0.043332722, -0.048530027, -0.10272868, 0.016439026, -0.0043581687, 0.014065921, 0.015250153, 0.0035983857, 0.024789328, 0.052941743, 0.0023809967, -0.0041563907, -0.02350335, -0.05152261, -0.026173577, 0.025396436, -0.020441707, 0.0052804356, 0.017074147, -0.023429962, 0.028667469, -0.056579348, 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建構嵌入資料庫
以下提供三個用於建構嵌入資料庫的範例文字。您將使用 Gemini API 來建立每個文件的嵌入。將結果轉換為資料框架，以獲得更佳的視覺化效果。
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]
將字典的內容整理成資料框架，提升視覺化效果。
df = pd.DataFrame(documents)
df.columns = ['Title', 'Text']
df
取得這些文字的嵌入。將這些資訊新增至 DataFrame。
# 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
透過問與答功能搜尋文件
嵌入內容已產生後，接著要建立問答系統來搜尋這些文件。您將提出有關超參數調整的問題、建立問題嵌入，並與 DataFrame 中的嵌入集合進行比較。
問題的嵌入會是向量 (浮點值清單)，將使用點積函數與文件的向量進行比較。從 API 傳回的向量已經正規化。內積代表兩個向量之間的方向相似度。
內積的值可介於 -1 至 1 之間 (含首尾)。如果兩個向量之間的點積為 1，則向量相同。如果內積值是 0，就表示這些向量互無關聯，或彼此無關。最後，如果點積為 -1，表示向量指向相反方向，不彼此相似。
請注意，使用新的嵌入模型 (embedding-001) 時，請將工作類型指定為使用者查詢的 QUERY，嵌入文件文字時則將工作類型指定為 DOCUMENT。
| 工作類型 | 說明 |
|---|---|
| RETRIEVAL_QUERY | 指出指定文字是搜尋/擷取設定中的查詢。 |
| RETRIEVAL_DOCUMENT | 指定指定文字是搜尋/擷取設定中的文件。 |
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")
使用 find_best_passage 函式計算內積，然後從大到最小的點產品值排序 DataFrame，從資料庫中擷取相關段落。
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
從資料庫中查看最相關的文件：
passage = find_best_passage(query, df)
passage
'Shifting Gears 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.'
問答應用程式
讓我們嘗試使用文字生成 API 建立問與答系統。在下方輸入您的自訂資料，即可建立簡單的問答範例。你仍須使用內積點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)
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: 'Shifting Gears 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:
選擇一種 Gemini 內容產生模型，找出問題的答案。
for m in genai.list_models():
if 'generateContent' in m.supported_generation_methods:
print(m.name)
models/gemini-pro models/gemini-pro-vision models/gemini-ultra
model = genai.GenerativeModel('gemini-1.5-pro-latest')
answer = model.generate_content(prompt)
Markdown(answer.text)
提供的段落未包含如何在 Google 車輛中移動齒輪的相關資訊，因此我無法從這個來源回答你的問題。
後續步驟
如要瞭解如何在 Gemini API 中使用其他服務，請參閱 Python 快速入門導覽課程。