Exemplo do modelo de embedding externo de Pesquisa Vetorial (OpenAI)

Este caderno mostra como usar o Vector Search Python SDK, que fornece um VectorSearchClient como API principal para trabalhar com Vector Search.

Este caderno utiliza o suporte do Databricks a modelos externos para aceder a um modelo de embeddings OpenAI e gerar embeddings.

%pip install --upgrade --force-reinstall databricks-vectorsearch tiktoken
dbutils.library.restartPython()

from databricks.vector_search.client import VectorSearchClient

vsc = VectorSearchClient(disable_notice=True)

# Display help for the Vector Search Client
help(VectorSearchClient)

Carregar o conjunto de dados de brinquedos na tabela Delta de origem

O seguinte cria a tabela Delta de origem.

# Specify the catalog and schema to use. You must have USE_CATALOG privilege on the catalog and USE_SCHEMA and CREATE_TABLE privileges on the schema.
# Change the catalog and schema here if necessary.

catalog_name = "main"
schema_name = "default"

source_table_name = "wiki_articles_demo"
source_table_fullname = f"{catalog_name}.{schema_name}.{source_table_name}"

# Uncomment the following line if you want to start from scratch.

# spark.sql(f"DROP TABLE {source_table_fullname}")

source_df = spark.read.parquet("/databricks-datasets/wikipedia-datasets/data-001/en_wikipedia/articles-only-parquet").limit(10)
display(source_df)

Conjunto de dados de amostra de fragmentos

Dividir o conjunto de dados da amostra ajuda-o a evitar ultrapassar o limite de contexto do modelo de embedding. O modelo OpenAI suporta até 8192 tokens. No entanto, o Databricks recomenda que divida os dados em blocos de contexto mais pequenos para que possa introduzir uma maior variedade de exemplos no modelo de raciocínio da sua aplicação RAG.

import tiktoken
import pandas as pd


max_chunk_tokens = 1024
encoding = tiktoken.get_encoding("cl100k_base")


def chunk_text(text):
    # Encode and then decode within the UDF
    tokens = encoding.encode(text)
    chunks = []
    while tokens:
        chunk_tokens = tokens[:max_chunk_tokens]
        chunk_text = encoding.decode(chunk_tokens)
        chunks.append(chunk_text)
        tokens = tokens[max_chunk_tokens:]
    return chunks

# Process the data and store in a new list
pandas_df = source_df.toPandas()
processed_data = []
for index, row in pandas_df.iterrows():
    text_chunks = chunk_text(row['text'])
    chunk_no = 0
    for chunk in text_chunks:
        row_data = row.to_dict()

        # Replace the id column with a new unique chunk id
        # and the text column with the text chunk
        row_data['id'] = f"{row['id']}_{chunk_no}"
        row_data['text'] = chunk

        processed_data.append(row_data)
        chunk_no += 1

chunked_pandas_df = pd.DataFrame(processed_data)
chunked_spark_df = spark.createDataFrame(chunked_pandas_df)

# Write the chunked DataFrame to a Delta table
spark.sql(f"DROP TABLE IF EXISTS {source_table_fullname}")
chunked_spark_df.write.format("delta") \
    .option("delta.enableChangeDataFeed", "true") \
    .saveAsTable(source_table_fullname)

display(spark.sql(f"SELECT * FROM {source_table_fullname}"))

Criar endpoint de pesquisa vetorial

vector_search_endpoint_name = "vector-search-demo-endpoint"

vsc.create_endpoint(
    name=vector_search_endpoint_name,
    endpoint_type="STANDARD" # or "STORAGE_OPTIMIZED"
)

vsc.get_endpoint(
  name=vector_search_endpoint_name
)

Registrar o endpoint do modelo de embedding OpenAI

Para informações detalhadas sobre o uso, consulte a documentação de modelos externos para configurar um endpoint OpenAI.

Para fornecer credenciais, utilize o gestor de segredos Databricks.

embedding_model_endpoint_name = "openai-embedding-endpoint"

import mlflow.deployments

mlflow_deploy_client = mlflow.deployments.get_deploy_client("databricks")

# Configure the secret manager with the OpenAPI key and provide the
# correct scope and key name below.

mlflow_deploy_client.create_endpoint(
    name=embedding_model_endpoint_name,
    config={
        "served_entities": [{
            "external_model": {
                "name": "text-embedding-ada-002",
                "provider": "openai",
                "task": "llm/v1/embeddings",
                "openai_config": {
                    "openai_api_key": "{{secrets/demo/openai-api-key}}" # CHANGE ME
                }
            }
    }]
    }
)

Criar índice vetorial

# Vector index
vs_index = f"{source_table_name}_openai_index"
vs_index_fullname = f"{catalog_name}.{schema_name}.{vs_index}"

index = vsc.create_delta_sync_index(
  endpoint_name=vector_search_endpoint_name,
  source_table_name=source_table_fullname,
  index_name=vs_index_fullname,
  pipeline_type='TRIGGERED',
  primary_key="id",
  embedding_source_column="text",
  embedding_model_endpoint_name=embedding_model_endpoint_name
)
index.describe()['status']['message']

# Wait for index to come online. Expect this command to take several minutes.
# You can also track the status of the index build in Catalog Explorer in the
# Overview tab for the vector index.

import time
index = vsc.get_index(endpoint_name=vector_search_endpoint_name,index_name=vs_index_fullname)
while not index.describe().get('status')['ready']:
  print("Waiting for index to be ready...")
  time.sleep(30)
print("Index is ready!")
index.describe()

Pesquisa por similaridade

As células seguintes mostram como consultar o Índice Vetorial para encontrar documentos semelhantes.

results = index.similarity_search(
  query_text="Greek myths",
  columns=["id", "text", "title"],
  num_results=5
  )
rows = results['result']['data_array']
for (id, text, title, score) in rows:
  if len(text) > 32:
    # trim text output for readability
    text = text[0:32] + "..."
  print(f"id: {id}  title: {title} text: '{text}' score: {score}")

# Search with a filter. Note that the syntax depends on the endpoint type.

# Standard endpoint syntax
results = index.similarity_search(
  query_text="Greek myths",
  columns=["id", "text", "title"],
  num_results=5,
  filters={"title NOT": "Hercules"}
)

# Storage-optimized endpoint syntax
# results = index.similarity_search(
#   query_text="Greek myths",
#   columns=["id", "text", "title"],
#   num_results=5,
#   filters='title != "Hercules"'
#   )

rows = results['result']['data_array']
for (id, text, title, score) in rows:
  if len(text) > 32:
    # trim text output for readability
    text = text[0:32] + "..."
  print(f"id: {id}  title: {title} text: '{text}' score: {score}")

Excluir índice vetorial

vsc.delete_index(
  endpoint_name=vector_search_endpoint_name,
  index_name=vs_index_fullname
)

Bloco de notas de exemplo

Exemplo do modelo externo de embedding com pesquisa vetorial (OpenAI)

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