Azure OpenAI¶
Azure OpenAI 资源不幸与标准 OpenAI 资源不同，因为除非使用嵌入模型，否则无法生成嵌入。这些模型可用的区域可以在这里找到：https://learn.microsoft.com/en-us/azure/cognitive-services/openai/concepts/models#embeddings-models
此外，支持嵌入模型的区域不幸不支持最新版本的 OpenAI 模型（<*>-003），因此我们不得不使用一个区域用于嵌入，另一个区域用于文本生成。
如果您在 Colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。
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%pip install llama-index-embeddings-azure-openai
%pip install llama-index-llms-azure-openai
%pip install llama-index-embeddings-azure-openai %pip install llama-index-llms-azure-openai
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!pip install llama-index
!pip install llama-index
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from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
import logging
import sys
logging.basicConfig(
stream=sys.stdout, level=logging.INFO
) # logging.DEBUG for more verbose output
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.llms.azure_openai import AzureOpenAI from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding from llama_index.core import VectorStoreIndex, SimpleDirectoryReader import logging import sys logging.basicConfig( stream=sys.stdout, level=logging.INFO ) # logging.DEBUG for more verbose output logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
在这里，我们设置嵌入模型（用于检索）和 LLM（用于文本生成）。请注意，您不仅需要模型名称（例如 "text-embedding-ada-002"），还需要模型部署名称（即您在 Azure 中部署模型时选择的名称）。初始化 AzureOpenAI 和 OpenAIEmbedding 时，必须将部署名称作为参数传入。
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api_key = "<api-key>"
azure_endpoint = "https://<your-resource-name>.openai.azure.com/"
api_version = "2023-07-01-preview"
llm = AzureOpenAI(
model="gpt-35-turbo-16k",
deployment_name="my-custom-llm",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
model="text-embedding-ada-002",
deployment_name="my-custom-embedding",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
api_key = "" azure_endpoint = "https://.openai.azure.com/" api_version = "2023-07-01-preview" llm = AzureOpenAI( model="gpt-35-turbo-16k", deployment_name="my-custom-llm", api_key=api_key, azure_endpoint=azure_endpoint, api_version=api_version, ) # 您需要部署自己的嵌入模型以及自己的聊天补全模型 embed_model = AzureOpenAIEmbedding( model="text-embedding-ada-002", deployment_name="my-custom-embedding", api_key=api_key, azure_endpoint=azure_endpoint, api_version=api_version, )
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from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
from llama_index.core import Settings Settings.llm = llm Settings.embed_model = embed_model
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documents = SimpleDirectoryReader(
input_files=["../../data/paul_graham/paul_graham_essay.txt"]
).load_data()
index = VectorStoreIndex.from_documents(documents)
documents = SimpleDirectoryReader( input_files=["../../data/paul_graham/paul_graham_essay.txt"] ).load_data() index = VectorStoreIndex.from_documents(documents)
INFO:httpx:HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK" HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK" HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK" INFO:httpx:HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK" HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK" HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK"
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query = "What is most interesting about this essay?"
query_engine = index.as_query_engine()
answer = query_engine.query(query)
print(answer.get_formatted_sources())
print("query was:", query)
print("answer was:", answer)
query = "What is most interesting about this essay?" query_engine = index.as_query_engine() answer = query_engine.query(query) print(answer.get_formatted_sources()) print("query was:", query) print("answer was:", answer)
INFO:httpx:HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK" HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK" HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-embedding/embeddings?api-version=2023-07-01-preview "HTTP/1.1 200 OK" INFO:httpx:HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-llm/chat/completions?api-version=2023-07-01-preview "HTTP/1.1 200 OK" HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-llm/chat/completions?api-version=2023-07-01-preview "HTTP/1.1 200 OK" HTTP Request: POST https://test-simon.openai.azure.com//openai/deployments/my-custom-llm/chat/completions?api-version=2023-07-01-preview "HTTP/1.1 200 OK" > Source (Doc id: 3e0d1e3f-9099-483f-9abd-8f352c5e730f): A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive feature... > Source (Doc id: 06c1d986-1856-44cd-980d-651252ad1caf): What I Worked On February 2021 Before college the two main things I worked on, outside of schoo... query was: What is most interesting about this essay? answer was: The most interesting aspect of this essay is the author's exploration of the transformative power of publishing essays online. The author reflects on how the internet has democratized the publishing process, allowing anyone to publish their work and reach a wide audience. This realization led the author to start writing and publishing essays online, which eventually became a significant part of their work. The author also discusses the initial skepticism and lack of prestige associated with online essays, but they find encouragement in the potential for genuine discovery and the absence of the desire to impress others. Overall, the essay highlights the author's personal journey and the impact of online publishing on their career.
