fix link, remove some code

Author: sdgilley

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

@@ -87,17 +87,6 @@ You can also use the class `AzureAIChatCompletionsModel` directly.
 [!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=create_client)]
 
 
-```python
-import os
-from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
-
-model = AzureAIChatCompletionsModel(
-    endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
-    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model="mistral-medium-2505",
-)
-```
-
 > [!CAUTION]
 > **Breaking change:** Parameter `model_name` was renamed `model` in version `0.1.3`.
 
@@ -151,17 +140,6 @@ Let's first use the model directly. `ChatModels` are instances of LangChain `Run
 
 [!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=human_message)] 
 
-```python
-from langchain_core.messages import HumanMessage, SystemMessage
-
-messages = [
-    SystemMessage(content="Translate the following from English into Italian"),
-    HumanMessage(content="hi!"),
-]
-
-model.invoke(messages)
-```
-
 You can also compose operations as needed in **chains**. Let's now use a prompt template to translate sentences:
 
 ```python
@@ -178,11 +156,6 @@ As you can see from the prompt template, this chain has a `language` and `text`
 
 [!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=create_output_parser)]
 
-```python
-from langchain_core.prompts import ChatPromptTemplate
-parser = StrOutputParser()
-```
-
 We can now combine the template, model, and the output parser from above using the pipe (`|`) operator:
 
 ```python
@@ -195,10 +168,6 @@ To invoke the chain, identify the inputs required and provide values using the `
 chain.invoke({"language": "italian", "text": "hi"})
 ```
 
-```output
-'ciao'
-```
-
 ### Chaining multiple LLMs together
 
 Models deployed to Azure AI Foundry support the Model Inference API, which is standard across all the models. Chain multiple LLM operations based on the capabilities of each model so you can optimize for the right model based on capabilities. 
@@ -207,21 +176,6 @@ In the following example, we create two model clients. One is a producer and ano
 
 [!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=create_producer_verifier)]
 
-```python
-from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
-
-producer = AzureAIChatCompletionsModel(
-    endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
-    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model="mistral-medium-2505",
-)
-
-verifier = AzureAIChatCompletionsModel(
-    endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
-    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model="mistral-small",
-)
-```
 
 > [!TIP]
 > Explore the model card of each of the models to understand the best use cases for each model.
@@ -230,40 +184,12 @@ The following example generates a poem written by an urban poet:
 
 [!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=generate_poem)]
 
-```python
-from langchain_core.prompts import PromptTemplate
-
-producer_template = PromptTemplate(
-    template="You are an urban poet, your job is to come up \
-             verses based on a given topic.\n\
-             Here is the topic you have been asked to generate a verse on:\n\
-             {topic}",
-    input_variables=["topic"],
-)
-
-verifier_template = PromptTemplate(
-    template="You are a verifier of poems, you are tasked\
-              to inspect the verses of poem. If they consist of violence and abusive language\
-              report it. Your response should be only one word either True or False.\n \
-              Here is the lyrics submitted to you:\n\
-              {input}",
-    input_variables=["input"],
-)
-```
-
 Now let's chain the pieces:
 
 [!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=create_chain)]
 
-```python
-chain = producer_template | producer | parser | verifier_template | verifier | parser
-```
-
 The previous chain returns the output of the step `verifier` only. Since we want to access the intermediate result generated by the `producer`, in LangChain you need to use a `RunnablePassthrough` object to also output that intermediate step. 
 
-[!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=create_chain_with_passthrough)]
-[!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=create_multiple_outputs_chain)]
-
 ```python
 from langchain_core.runnables import RunnablePassthrough, RunnableParallel
 
@@ -277,10 +203,6 @@ To invoke the chain, identify the inputs required and provide values using the `
 
 [!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb?name=invoke_chain)]
 
-```python
-chain.invoke({"topic": "living in a foreign country"})
-```
-
 
 ## Use embeddings models
 
@@ -318,7 +240,7 @@ vector_store = InMemoryVectorStore(embed_model)
 
 Let's add some documents:
 
-[!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-embeddings.ipynb?name="add_documents)]
+[!notebook-python[](~/azureai-samples-main/scenarios/langchain/getting-started-with-langchain-embeddings.ipynb?name=add_documents)]
 
 ```python
 from langchain_core.documents import Document