mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
[BREAKING] Python: fix OpenAI Azure routing and provider samples (#4925)
* Python: fix OpenAI Azure routing and provider samples Prefer OpenAI when OPENAI_API_KEY is present unless Azure is explicitly requested. Clarify constructor docs, keep deprecated Azure wrappers compatible with stricter settings validation, and refresh the provider samples and tests to use the current client patterns. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix bandit * Python: align OpenAI embedding Azure routing Extend the shared OpenAI-vs-Azure routing and credential behavior to the embedding client, add Azure embedding regression coverage, and refresh the embedding samples to use the generic client path. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: fix embedding client pyright check Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: thin OpenAI embedding wrapper Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: document embedding overload routing Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: fix callable OpenAI key routing Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: fix Azure credential routing tests Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: address OpenAI review feedback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: narrow Azure routing markers Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: refine OpenAI model fallback order Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: narrow Azure deployment docs Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: remove embedding routing wording Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: run embedding Azure integration tests Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * changed variable name * Python: expand OpenAI package README Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * clarified readme * Python: fix Azure OpenAI integration setup Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: correct Azure integration env mapping Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated code to fix int tests * test updates * test fix * fix test setup * updates to tests and setup * remove openai assistants int tests * improvements in int tests * fix env var * fix env vars * fix azure responses test * trigger actions --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
committed by
GitHub
Unverified
GitHub
parent
3611be82cf
commit
cc0cfaaac8
@@ -2,55 +2,59 @@
|
||||
|
||||
# Run with: uv run samples/02-agents/embeddings/azure_openai_embeddings.py
|
||||
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from agent_framework.azure import AzureOpenAIEmbeddingClient
|
||||
from agent_framework.openai import OpenAIEmbeddingClient
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
"""Azure OpenAI Embedding Client Example
|
||||
|
||||
This sample demonstrates how to generate embeddings using the Azure OpenAI embedding client.
|
||||
It supports both API key and Azure credential authentication.
|
||||
"""This sample demonstrates Azure OpenAI embedding generation with ``OpenAIEmbeddingClient``.
|
||||
|
||||
Prerequisites:
|
||||
Set the following environment variables or add them to a .env file:
|
||||
- AZURE_OPENAI_ENDPOINT: Your Azure OpenAI endpoint URL
|
||||
- AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: The embedding model deployment name
|
||||
- AZURE_OPENAI_API_KEY: Your API key (or use Azure credential instead)
|
||||
Set the following environment variables or add them to a local ``.env`` file:
|
||||
- ``AZURE_OPENAI_ENDPOINT``: Your Azure OpenAI endpoint URL
|
||||
- ``AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME``: The embedding deployment name
|
||||
- ``AZURE_OPENAI_API_VERSION``: Optional API version override
|
||||
|
||||
Sign in with ``az login`` before running the sample.
|
||||
"""
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
"""Generate embeddings with Azure OpenAI."""
|
||||
# 1. Create a client using environment variables.
|
||||
# Reads AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME,
|
||||
# and AZURE_OPENAI_API_KEY from environment.
|
||||
client = AzureOpenAIEmbeddingClient()
|
||||
async with AzureCliCredential() as credential:
|
||||
client = OpenAIEmbeddingClient(
|
||||
model=os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME"),
|
||||
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
|
||||
api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
|
||||
credential=credential,
|
||||
)
|
||||
|
||||
# 2. Generate a single embedding.
|
||||
result = await client.get_embeddings(["Hello, world!"])
|
||||
print(f"Single embedding dimensions: {result[0].dimensions}")
|
||||
print(f"First 5 values: {result[0].vector[:5]}")
|
||||
print(f"Model: {result[0].model_id}")
|
||||
print(f"Usage: {result.usage}")
|
||||
print()
|
||||
# 1. Generate a single embedding.
|
||||
result = await client.get_embeddings(["Hello, world!"])
|
||||
print(f"Single embedding dimensions: {result[0].dimensions}")
|
||||
print(f"First 5 values: {result[0].vector[:5]}")
|
||||
print(f"Model: {result[0].model}")
|
||||
print(f"Usage: {result.usage}")
|
||||
print()
|
||||
|
||||
# 3. Generate embeddings for multiple inputs.
|
||||
texts = [
|
||||
"The weather is sunny today.",
|
||||
"It is raining outside.",
|
||||
"Machine learning is fascinating.",
|
||||
]
|
||||
result = await client.get_embeddings(texts)
|
||||
print(f"Batch of {len(result)} embeddings, each with {result[0].dimensions} dimensions")
|
||||
print()
|
||||
# 2. Generate embeddings for multiple inputs.
|
||||
texts = [
|
||||
"The weather is sunny today.",
|
||||
"It is raining outside.",
|
||||
"Machine learning is fascinating.",
|
||||
]
|
||||
result = await client.get_embeddings(texts)
|
||||
print(f"Batch of {len(result)} embeddings, each with {result[0].dimensions} dimensions")
|
||||
print(f"First embedding vector: {result[0].vector[:5]}")
|
||||
print()
|
||||
|
||||
# 4. Generate embeddings with custom dimensions.
|
||||
result = await client.get_embeddings(["Custom dimensions example"], options={"dimensions": 256})
|
||||
print(f"Custom dimensions: {result[0].dimensions}")
|
||||
# 3. Generate embeddings with custom dimensions.
|
||||
result = await client.get_embeddings(["Custom dimensions example"], options={"dimensions": 256})
|
||||
print(f"Custom dimensions: {result[0].dimensions}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@@ -3,31 +3,32 @@
|
||||
# Run with: uv run samples/02-agents/embeddings/openai_embeddings.py
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from agent_framework.openai import OpenAIEmbeddingClient
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
"""OpenAI Embedding Client Example
|
||||
|
||||
This sample demonstrates how to generate embeddings using the OpenAI embedding client.
|
||||
It shows single and batch embedding generation, as well as custom dimensions.
|
||||
"""This sample demonstrates OpenAI embedding generation with explicit constructor settings.
|
||||
|
||||
Prerequisites:
|
||||
Set the OPENAI_API_KEY environment variable or add it to a .env file.
|
||||
Set ``OPENAI_API_KEY`` in your environment or in a local ``.env`` file.
|
||||
"""
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
"""Generate embeddings with OpenAI."""
|
||||
client = OpenAIEmbeddingClient(model="text-embedding-3-small")
|
||||
client = OpenAIEmbeddingClient(
|
||||
model="text-embedding-3-small",
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
)
|
||||
|
||||
# 1. Generate a single embedding.
|
||||
result = await client.get_embeddings(["Hello, world!"])
|
||||
print(f"Single embedding dimensions: {result[0].dimensions}")
|
||||
print(f"First 5 values: {result[0].vector[:5]}")
|
||||
print(f"Model: {result[0].model_id}")
|
||||
print(f"Model: {result[0].model}")
|
||||
print(f"Usage: {result.usage}")
|
||||
print()
|
||||
|
||||
@@ -39,7 +40,7 @@ async def main() -> None:
|
||||
]
|
||||
result = await client.get_embeddings(texts)
|
||||
print(f"Batch of {len(result)} embeddings, each with {result[0].dimensions} dimensions")
|
||||
print(f"First embedding vector: {result[0].vector[:5]}") # Print first 5 values of the first embedding
|
||||
print(f"First embedding vector: {result[0].vector[:5]}")
|
||||
print()
|
||||
|
||||
# 3. Generate embeddings with custom dimensions.
|
||||
|
||||
Reference in New Issue
Block a user