AutoAI RAG 实验编码 Python

接地文档

准备并连接用于运行 RAG 实验的接地文件。 有关详细信息，请参阅 在项目中获取和准备数据。

• 支持的格式：PDF、HTML、 DOCX、Markdown、纯文本
• 连接到 Cloud Object Storage 存储桶、存储桶中的文件夹或指定多达 20 个文件中的数据。
• AutoAI为运行实验提供文件样本

例如，当文档存储在 Cloud Object Storage 存储桶中时，创建数据连接：

from ibm_watsonx_ai.helpers import DataConnection, S3Location

datasource_name = 'bluemixcloudobjectstorage'

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: f"Connection to input data - {datasource_name} ",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_id_by_name(datasource_name),
    client.connections.ConfigurationMetaNames.DESCRIPTION: "ibm-watsonx-ai SDK documentation",
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': <BUCKET_NAME>,
        'access_key': <ACCESS_KEY>,
        'secret_key': <SECRET_ACCESS_KEY>,
        'iam_url': 'https://iam.cloud.ibm.com/identity/token',
        'url': <ENDPOINT_URL>
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)
cos_connection_id = client.connections.get_id(conn_details)

input_data_references = [
    DataConnection(
        connection_asset_id=cos_connection_id,
        location=S3Location(
            bucket=<BUCKET_NAME>,
            path=<BUCKET_PATH>
        )
    )
]

下面的示例演示了如何使用在项目中创建（或推广到空间）的数据资产：

import os, wget
from ibm_watsonx_ai.helpers import DataConnection

input_data_filename = "core_api.html"
input_data_path = f"https://ibm.github.io/watsonx-ai-python-sdk/{input_data_filename}"

if not os.path.isfile(input_data_filename):
    wget.download(input_data_path, out=input_data_filename)

asset_details = client.data_assets.create(input_data_filename, input_data_filename)
asset_id = client.data_assets.get_id(asset_details)

input_data_references = [DataConnection(data_asset_id=asset_id)]

求值数据

评估数据必须采用 JSON 格式，并具有包含这些字段的固定模式：question, correct_answer, correct_answer_document_ids

例如：

[
    {
        "question": "What is the purpose of get_token()?",
        "correct_answer": "get_token() is used to retrieve an authentication token for secure API access.",
        "correct_answer_document_ids": [
            "core_api.html"
        ]
    },
    {
        "question": "How does the delete_model() function operate?",
        "correct_answer": "delete_model() method allows users to delete models they've created or managed.",
        "correct_answer_document_ids": [
            "core_api.html"
        ]
    }
]

准备评估数据：

import os, wget
from ibm_watsonx_ai.helpers import DataConnection

test_data_filename = "benchmarking_data_core_api.json"
test_data_path = f"https://github.com/IBM/watsonx-ai-samples/tree/master/cloud/data/autoai_rag{test_data_filename}"

if not os.path.isfile(test_data_filename):
    wget.download(test_data_path, out=test_data_filename)

test_asset_details = client.data_assets.create(name=test_data_filename, file_path=test_data_filename)
test_asset_id = client.data_assets.get_id(test_asset_details)

test_data_references = [DataConnection(data_asset_id=test_asset_id)]

连接到 Milvus 矢量数据库

如果使用的是 Milvus 矢量数据库，请创建与矢量存储的连接。

from ibm_watsonx_ai.helpers import DataConnection

milvus_data_source_type_id = client.connections.get_datasource_type_uid_by_name("milvus")
details = client.connections.create(
    {
        client.connections.ConfigurationMetaNames.NAME: "Milvus Connection",
        client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: milvus_data_source_type_id,
        client.connections.ConfigurationMetaNames.PROPERTIES: {
            "host": <PASTE MILVUS HOST HERE>,
            "port": <PASTE MILVUS PORT HERE>,
            "username": <PASTE MILVUS USERNAME HERE>,
            "password": <PASTE MILVUS PASSWORD HERE>,
            "ssl": True,
        },
    }
)

milvus_connection_id = client.connections.get_id(details)
vector_store_references = [DataConnection(connection_asset_id=milvus_connection_id)]

选择一个模式进行本地测试

下一步是选择一种模式并在本地进行测试。

best_pattern = rag_optimizer.get_pattern()

from ibm_watsonx_ai.deployments import RuntimeContext

runtime_context = RuntimeContext(api_client=client)
inference_service = best_pattern.inference_service(runtime_context)[0]

question = "How to use new approach of providing credentials to APIClient?"

context = RuntimeContext(
    api_client=client,
    request_payload_json={"messages": [{"role": "user", "content": question}]},
)
print(inference_service(context)["body"]["choices"][0]["message"]["content"])

模特的回答：

According to the document, the new approach to provide credentials to APIClient is by using the Credentials class. Here's an example:


from ibm_watsonx_ai import APIClient
from ibm_watsonx_ai import Credentials

credentials = Credentials(
                   url = "https://us-south.ml.cloud.ibm.com",
                   token = "***********",
                  )

client = APIClient(credentials)


This replaces the old approach of passing a dictionary with credentials to the APIClient constructor.

测试已部署的模式

本代码示例演示了如何测试已部署的解决方案。 使用以下格式在有效载荷中输入测试问题：

payload = {
    "messages": [{"role": "user", "content": "How to use new approach of providing credentials to APIClient?"}]
}

score_response = client.deployments.run_ai_service(deployment_id, payload)
answer = score_response["choices"][0]["message"]["content"]
print(answer)

模特的回答：

According to the document, the new approach to provide credentials to APIClient is by using the Credentials class. Here's an example:


from ibm_watsonx_ai import APIClient
from ibm_watsonx_ai import Credentials

credentials = Credentials(
                   url = "https://us-south.ml.cloud.ibm.com",
                   token = "***********",
                  )

client = APIClient(credentials)


This replaces the old approach of passing a dictionary with credentials to the APIClient constructor.

获取推理和索引笔记本

要从服务处下载指定的推理笔记本，请使用 get_inference_notebook()。 如果将 pattern_name 留空，该方法将下载排名最高的模式的笔记本。

rag_optimizer.get_inference_notebook(pattern_name='Pattern3')