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articles/ai-foundry/how-to/develop/langchain.md

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@@ -249,12 +249,6 @@ Let's search by similarity:
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[!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-embeddings.ipynb?name=search_similarity)]
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```python
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results = vector_store.similarity_search(query="thud",k=1)
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for doc in results:
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print(f"* {doc.page_content} [{doc.metadata}]")
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```
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## Using Azure OpenAI models
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If you're using Azure OpenAI models with `langchain-azure-ai` package, use the following URL:
@@ -277,43 +271,11 @@ First, configure logging to the level you are interested in:
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[!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=configure_logging)]
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```python
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import sys
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import logging
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# Acquire the logger for this client library. Use 'azure' to affect both
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# 'azure.core` and `azure.ai.inference' libraries.
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logger = logging.getLogger("azure")
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# Set the desired logging level. logging.INFO or logging.DEBUG are good options.
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logger.setLevel(logging.DEBUG)
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# Direct logging output to stdout:
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handler = logging.StreamHandler(stream=sys.stdout)
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# Or direct logging output to a file:
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# handler = logging.FileHandler(filename="sample.log")
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logger.addHandler(handler)
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# Optional: change the default logging format. Here we add a timestamp.
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formatter = logging.Formatter("%(asctime)s:%(levelname)s:%(name)s:%(message)s")
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handler.setFormatter(formatter)
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```
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To see the payloads of the requests, when instantiating the client, pass the argument `logging_enable`=`True` to the `client_kwargs`:
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[!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=create_client_with_logging)]
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```python
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import os
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from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
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model = AzureAIChatCompletionsModel(
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endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
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credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
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model="mistral-medium-2505",
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client_kwargs={"logging_enable": True},
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)
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```
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Use the client as usual in your code.
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