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Búsqueda de documentos con incorporaciones

Ver en ai.google.dev

Prueba un notebook de Colab

Ver notebook en GitHub

Descripción general

En este ejemplo, se muestra cómo usar la API de Gemini para crear incorporaciones que te permitan realizar búsquedas de documentos. Usarás la biblioteca cliente de Python para compilar una incorporación de palabras que te permita comparar cadenas de búsqueda o preguntas con contenidos de documentos.

En este instructivo, usarás incorporaciones para realizar búsquedas de documentos en un conjunto de documentos para hacer preguntas relacionadas con el vehículo de Google.

Requisitos previos

Puedes ejecutar esta guía de inicio rápido en Google Colab.

Para completar esta guía de inicio rápido en tu entorno de desarrollo, asegúrate de que cumpla con los siguientes requisitos:

Python 3.9 y versiones posteriores
Una instalación de jupyter para ejecutar el notebook

Configuración

Primero, descarga e instala la biblioteca de Python de la API de 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

Obtén una clave de API

Para poder usar la API de Gemini, primero debes obtener una clave de API. Si aún no tienes una, crea una clave con un clic en Google AI Studio.

Obtén una clave de API.

En Colab, agrega la clave al administrador de Secrets en la "Notebook" que aparece a continuación. en el panel izquierdo. Asígnale el nombre API_KEY.

Una vez que tengas la clave de API, pásala al SDK. Puedes hacerlo de dos maneras:

Coloca la clave en la variable de entorno GOOGLE_API_KEY (el SDK la recogerá automáticamente desde allí).
Pasa la clave a genai.configure(api_key=...)

genai.configure(api_key=GOOGLE_API_KEY)

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

models/embedding-001
models/embedding-001

Generación de incorporaciones

En esta sección, verás cómo generar incorporaciones para un fragmento de texto con las incorporaciones de la API de Gemini.

Cambios en la API a las incorporaciones con el modelo embedding-001

Para el nuevo modelo de incorporaciones, embedding-001, hay un nuevo parámetro de tipo de tarea y el título opcional (solo válido con task_type=RETRIEVAL_DOCUMENT).

Estos parámetros nuevos se aplican solo a los modelos de incorporaciones más recientes.Los tipos de tareas son los siguientes:

Tipo de tarea	Descripción
RETRIEVAL_QUERY	Especifica que el texto dado es una consulta en un parámetro de configuración de búsqueda/recuperación.
RETRIEVAL_DOCUMENT	Especifica que el texto dado de un documento en un parámetro de configuración de búsqueda y recuperación.
SEMANTIC_SIMILARITY	Especifica que el texto dado se usará para la similitud textual semántica (STS).
CLASIFICACIÓN	Especifica que las incorporaciones se usarán para la clasificación.
Agrupamiento en clústeres	Especifica que las incorporaciones se usarán para el agrupamiento en clústeres.

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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Compila una base de datos de incorporaciones

Aquí hay tres textos de muestra que puedes usar para compilar la base de datos de incorporaciones. Usarás la API de Gemini para crear incorporaciones de cada uno de los documentos. Conviértelos en un marco de datos para una mejor visualización.

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]

Organizar los contenidos del diccionario en un marco de datos para una mejor visualización.

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

Obtén las incorporaciones para cada uno de estos cuerpos de texto. Agrega esta información al marco de datos.

# 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

Búsqueda de documentos con preguntas y respuestas

Ahora que se generaron las incorporaciones, crearemos un sistema de preguntas y respuestas para buscar en estos documentos. Harás una pregunta sobre el ajuste de hiperparámetros, crearás una incorporación de la pregunta y la compararás con la colección de incorporaciones en el marco de datos.

La incorporación de la pregunta será un vector (lista de valores flotantes), que se comparará con el vector de los documentos que usan el producto punto. Este vector que muestra la API ya está normalizado. El producto escalar representa la similitud en la dirección entre dos vectores.

Los valores del producto escalar pueden variar entre -1 y 1, ambos incluidos. Si el producto escalar entre dos vectores es 1, entonces los vectores están en la misma dirección. Si el valor del producto escalar es 0, estos vectores son ortogonales o no están relacionados entre sí. Por último, si el producto punto es -1, entonces los vectores apuntan en la dirección opuesta y no son similares entre sí.

Ten en cuenta que, con el nuevo modelo de incorporaciones (embedding-001), especifica el tipo de tarea como QUERY para la consulta del usuario y DOCUMENT cuando incorpores texto en un documento.

Tipo de tarea	Descripción
RETRIEVAL_QUERY	Especifica que el texto dado es una consulta en un parámetro de configuración de búsqueda/recuperación.
RETRIEVAL_DOCUMENT	Especifica que el texto dado de un documento en un parámetro de configuración de búsqueda y recuperación.

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")

Usa la función find_best_passage para calcular los productos escalar y, luego, ordena el marco de datos del valor de producto escalar de mayor al más pequeño para recuperar el pasaje relevante fuera de la base de datos.

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

Consulta el documento más relevante de la base de datos:

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.'

Solicitud de preguntas y respuestas

Intentemos usar la API de generación de texto para crear una sesión Un sistema Ingresa tus propios datos personalizados a continuación para crear una pregunta simple con un ejemplo de respuesta. Seguirás usando el producto escalar como una métrica de similitud.

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:

Elige uno de los modelos de generación de contenido de Gemini para encontrar la respuesta a tu consulta.

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)

El pasaje proporcionado no contiene información sobre cómo cambiar los cambios de un automóvil de Google, por lo que no puedo responder tu pregunta de esta fuente.

Próximos pasos

Para obtener más información sobre cómo usar las incorporaciones, consulta estos otros instructivos: