Update how-to-deploy-models-llama.md

Author: ssalgadodev

articles/machine-learning/how-to-deploy-models-llama.md

@@ -554,15 +554,6 @@ For reference about how to invoke Meta Llama 3 models deployed to real-time endp
 | Cohere SDK     | [cohere-sdk.ipynb](https://aka.ms/samples/cohere-embed/cohere-python-sdk)                                 |
 | LiteLLM SDK    | [litellm.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/cohere/litellm.ipynb) |
 
-##### Retrieval Augmented Generation (RAG) and tool use samples
-**Description** | **Package** | **Sample Notebook**
---|--|--
-Create a local Facebook AI Similarity Search (FAISS) vector index, using Cohere embeddings - Langchain|`langchain`, `langchain_cohere`|[cohere_faiss_langchain_embed.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/cohere/cohere_faiss_langchain_embed.ipynb)
-Use Cohere Command R/R+ to answer questions from data in local FAISS vector index - Langchain|`langchain`, `langchain_cohere`|[command_faiss_langchain.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/cohere/command_faiss_langchain.ipynb)
-Use Cohere Command R/R+ to answer questions from data in AI search vector index - Langchain|`langchain`, `langchain_cohere`|[cohere-aisearch-langchain-rag.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/cohere/cohere-aisearch-langchain-rag.ipynb)
-Use Cohere Command R/R+ to answer questions from data in AI search vector index - Cohere SDK| `cohere`, `azure_search_documents`|[cohere-aisearch-rag.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/cohere/cohere-aisearch-rag.ipynb)
-Command R+ tool/function calling, using LangChain|`cohere`, `langchain`, `langchain_cohere`|[command_tools-langchain.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/cohere/command_tools-langchain.ipynb)
-
 ## Cost and quotas
 
 ### Cost and quota considerations for Meta Llama models deployed as a serverless API