Ricerca di documenti con incorporamenti

Visualizza su ai.google.dev

Esegui in Google Colab

Visualizza il codice sorgente su GitHub

Panoramica

Questo esempio mostra come utilizzare l'API Gemini per creare incorporamenti in modo da poter eseguire ricerche nei documenti. Utilizzerai la libreria client Python per creare un incorporamento di parole che ti consenta di confrontare stringhe di ricerca o domande con i contenuti dei documenti.

In questo tutorial, utilizzerai gli incorporamenti per eseguire ricerche su un insieme di documenti e porre domande relative a Google Car.

Prerequisiti

Puoi eseguire questa guida rapida in Google Colab.

Per completare questa guida rapida sul tuo ambiente di sviluppo, assicurati che l'ambiente soddisfi i seguenti requisiti:

• Python 3.9 o versioni successive
• Un'installazione di jupyter per eseguire il blocco note.

Configurazione

Innanzitutto, scarica e installa la libreria Python dell'API Gemini.

pip install -U -q google.generativeai

import textwrap
import numpy as np
import pandas as pd

import google.generativeai as genai

# Used to securely store your API key
from google.colab import userdata

from IPython.display import Markdown

Procurati una chiave API

Prima di poter utilizzare l'API Gemini, devi ottenere una chiave API. Se non ne hai già una, crea una chiave con un solo clic in Google AI Studio.

Ottenere una chiave API

In Colab, aggiungi la chiave a Secret Manager nella sezione " {/8}" nel riquadro a sinistra. Assegnagli il nome API_KEY.

Una volta ottenuta la chiave API, passala all'SDK. A tale scopo, puoi procedere in uno dei due seguenti modi:

• Inserisci la chiave nella variabile di ambiente GOOGLE_API_KEY (l'SDK la acquisirà automaticamente da lì).
• Passa la chiave a 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

Generazione degli incorporamenti

In questa sezione, vedrai come generare incorporamenti per una porzione di testo utilizzando gli incorporamenti dell'API Gemini.

Modifiche all'API per gli incorporamenti con il modello Embedding-001

Per il nuovo modello degli incorporamenti, Embedding-001, sono disponibili un nuovo parametro per il tipo di attività e il titolo facoltativo (valido solo con task_type=RETRIEVAL_DOCUMENT).

Questi nuovi parametri si applicano solo ai modelli di incorporamenti più recenti.I tipi di attività sono:

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, 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Creazione di un database degli incorporamenti

Ecco tre testi di esempio da utilizzare per creare il database degli incorporamenti. Utilizzerai l'API Gemini per creare incorporamenti di ciascuno dei documenti. Trasformali in un dataframe per una visualizzazione migliore.

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]

Organizza i contenuti del dizionario in un dataframe per una migliore visualizzazione.

df = pd.DataFrame(documents)
df.columns = ['Title', 'Text']
df

Ottieni gli incorporamenti per ciascuno di questi corpi di testo. Aggiungi queste informazioni al 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

Ricerca di documenti con Domande e risposte

Ora che gli incorporamenti sono stati generati, creiamo un sistema di domande e risposte per cercare in questi documenti. Dovrai fare una domanda sull'ottimizzazione degli iperparametri, creare un incorporamento della domanda e confrontarlo con la raccolta di incorporamenti nel dataframe.

L'incorporamento della domanda sarà un vettore (elenco di valori in virgola mobile), che verrà confrontato con il vettore dei documenti utilizzando il prodotto scalare. Questo vettore restituito dall'API è già normalizzato. Il prodotto scalare rappresenta la somiglianza nella direzione tra due vettori.

I valori del prodotto scalare possono essere compresi tra -1 e 1 inclusi. Se il prodotto scalare tra due vettori è 1, i vettori sono nella stessa direzione. Se il valore del prodotto scalare è 0, questi vettori sono ortogonali o non correlati tra loro. Infine, se il prodotto scalare è -1, i vettori puntano nella direzione opposta e non sono simili tra loro.

Nota, con il nuovo modello degli incorporamenti (embedding-001), specifica il tipo di attività come QUERY per la query dell'utente e DOCUMENT quando incorpori il testo di un documento.

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")

Utilizza la funzione find_best_passage per calcolare i prodotti scalare, quindi ordina il dataframe dal valore del prodotto scalare più grande al più piccolo per recuperare il passaggio pertinente dal database.

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

Visualizza il documento più pertinente dal database:

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.'

Applicazione Question and Answering

Proviamo a utilizzare l'API Text Generation per creare una sessione Un sistema. Inserisci i tuoi dati personalizzati di seguito per creare una semplice domanda e un esempio di risposta. Continuerai a utilizzare il prodotto scalare come metrica di somiglianza.

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:

Scegli uno dei modelli di generazione di contenuti di Gemini per trovare la risposta alla tua query.

for m in genai.list_models():
  if 'generateContent' in m.supported_generation_methods:
    print(m.name)

models/gemini-pro
models/gemini-ultra

model = genai.GenerativeModel('gemini-1.5-pro-latest')
answer = model.generate_content(prompt)

Markdown(answer.text)

Il passaggio fornito non contiene informazioni su come cambiare marcia in un'auto Google, quindi non posso rispondere alla tua domanda da questa fonte.

Passaggi successivi

Per saperne di più su come utilizzare gli incorporamenti, guarda questi altri tutorial:

• Rilevamento di anomalie con incorporamenti
• Cluster con incorporamenti
• Addestramento di un classificatore di testo con incorporamenti