Update scan llm docs

docs/open_source/scan/scan_llm/index.md

@@ -38,18 +38,13 @@ processed.
 
 ## Before starting
 
-In the following example, we illustrate the procedure using **OpenAI** and **Azure OpenAI**; however, please note that
-our platform supports a variety of language models. For details on configuring different models, visit
-our [🤖 Setting up the LLM Client page](../../setting_up/index.md)
-
-Before starting, make sure you have installed the LLM flavor of Giskard:
+First of all, make sure you have installed the LLM flavor of Giskard:
 
 ```bash
 pip install "giskard[llm]"
 ```
 
-For the LLM-assisted detectors to work, you need to have an OpenAI API key. You can set it in your notebook
-like this:
+For the LLM-assisted detectors to work, you need to set up a LLM client. Our platform supports a variety of language models, and you can find the details on configuring different models in our [🤖 Setting up the LLM Client page](../../setting_up/index.md) or follow the instructions below for each provider:
 
 :::::::{tab-set}
 ::::::{tab-item} OpenAI
@@ -58,18 +53,18 @@ like this:
 import os
 import giskard
 
-os.environ["OPENAI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_KEY"] = "" # "my-openai-api-key"
 
-# Optional, setup a model (default model is gpt-4)
-giskard.llm.set_llm_model("gpt-4")
-giskard.llm.set_embedding_model("text-embedding-ada-002")
+# Optional, setup a model (default LLM is gpt-4o, default embedding model is text-embedding-3-small)
+giskard.llm.set_llm_model("gpt-4o")
+giskard.llm.set_embedding_model("text-embedding-3-small")
 
 # Optional Keys - OpenAI Organization, OpenAI API Base
-os.environ["OPENAI_ORGANIZATION"] = "your-org-id"
-os.environ["OPENAI_API_BASE"] = "openaiai-api-base"
+os.environ["OPENAI_ORGANIZATION"] = "" # "my-openai-organization"
+os.environ["OPENAI_API_BASE"] = "" # "https://api.openai.com"
 ```
 
-More information on [LiteLLM documentation](https://docs.litellm.ai/docs/providers/openai)
+More information on [OpenAI LiteLLM documentation](https://docs.litellm.ai/docs/providers/openai)
 
 ::::::
 ::::::{tab-item} Azure OpenAI
@@ -82,16 +77,15 @@ os.environ["AZURE_API_KEY"] = "" # "my-azure-api-key"
 os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
 os.environ["AZURE_API_VERSION"] = "" # "2023-05-15"
 
-giskard.llm.set_llm_model("azure/<your_deployment_name>")
-giskard.llm.set_embedding_model("azure/<your_deployment_name>")
+giskard.llm.set_llm_model("azure/<your_llm_name>")
+giskard.llm.set_embedding_model("azure/<your_embed_model_name>")
 
-# optional
+# Optional Keys - Azure AD Token, Azure API Type
 os.environ["AZURE_AD_TOKEN"] = ""
 os.environ["AZURE_API_TYPE"] = ""
-giskard.llm.set_embedding_model('my-embedding-model')
 ```
 
-More information on [LiteLLM documentation](https://docs.litellm.ai/docs/providers/azure)
+More information on [Azure LiteLLM documentation](https://docs.litellm.ai/docs/providers/azure)
 
 ::::::
 ::::::{tab-item} Mistral
@@ -100,99 +94,105 @@ More information on [LiteLLM documentation](https://docs.litellm.ai/docs/provide
 import os
 import giskard
 
-os.environ['MISTRAL_API_KEY'] = ""
+os.environ["MISTRAL_API_KEY"] = "" # "my-mistral-api-key"
 
-giskard.llm.set_llm_model("mistral/mistral-tiny")
+giskard.llm.set_llm_model("mistral/mistral-large-latest")
 giskard.llm.set_embedding_model("mistral/mistral-embed")
 ```
 
-More information on [LiteLLM documentation](https://docs.litellm.ai/docs/providers/mistral)
+More information on [Mistral LiteLLM documentation](https://docs.litellm.ai/docs/providers/mistral)
 
 ::::::
 ::::::{tab-item} Ollama
 
 ```python
 import giskard
 
-giskard.llm.set_llm_model("ollama/llama2", api_base="http://localhost:11434") # See supported models here: https://docs.litellm.ai/docs/providers/ollama#ollama-models
-```
+api_base = "http://localhost:11434" # default api_base for local Ollama
 
-More information on [LiteLLM documentation](https://docs.litellm.ai/docs/providers/ollama)
+# See supported models here: https://docs.litellm.ai/docs/providers/ollama#ollama-models
+giskard.llm.set_llm_model("ollama/llama3", api_base=api_base)
+giskard.llm.set_embedding_model("ollama/nomic-embed-text", api_base=api_base)
+```
 
+More information on [Ollama LiteLLM documentation](https://docs.litellm.ai/docs/providers/ollama)
 
 ::::::
 ::::::{tab-item} AWS Bedrock
 
-More information on [LiteLLM documentation](https://docs.litellm.ai/docs/providers/bedrock)
+More information on [Bedrock LiteLLM documentation](https://docs.litellm.ai/docs/providers/bedrock)
 
 ```python
 import os
 import giskard
 
-os.environ["AWS_ACCESS_KEY_ID"] = ""
-os.environ["AWS_SECRET_ACCESS_KEY"] = ""
-os.environ["AWS_REGION_NAME"] = ""
+os.environ["AWS_ACCESS_KEY_ID"] = "" # "my-aws-access-key"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "" # "my-aws-secret-access-key"
+os.environ["AWS_REGION_NAME"] = "" # "us-west-2"
 
 giskard.llm.set_llm_model("bedrock/anthropic.claude-3-sonnet-20240229-v1:0")
-giskard.llm.set_embedding_model("bedrock/amazon.titan-embed-text-v1")
+giskard.llm.set_embedding_model("bedrock/amazon.titan-embed-image-v1")
 ```
 
 ::::::
 ::::::{tab-item} Gemini
 
-More information on [LiteLLM documentation](https://docs.litellm.ai/docs/providers/gemini)
+More information on [Gemini LiteLLM documentation](https://docs.litellm.ai/docs/providers/gemini)
 
 ```python
 import os
 import giskard
 
-os.environ["GEMINI_API_KEY"] = "your-api-key"
+os.environ["GEMINI_API_KEY"] = "" # "my-gemini-api-key"
 
 giskard.llm.set_llm_model("gemini/gemini-pro")
+giskard.llm.set_embedding_model("gemini/text-embedding-004")
 ```
 
 ::::::
 ::::::{tab-item} Custom Client
 
-More information on [LiteLLM documentation](https://docs.litellm.ai/docs/providers/custom_llm_server    )
+More information on [Custom Format LiteLLM documentation](https://docs.litellm.ai/docs/providers/custom_llm_server)
 
 ```python
-import requests
-import giskard
-import litellm
 import os
+import requests
 from typing import Optional
 
+import litellm
+import giskard
+
 
 class MyCustomLLM(litellm.CustomLLM):
-  def completion(self, messages: str, api_key: Optional[str] = None, **kwargs) -> litellm.ModelResponse:
-    api_key = api_key or os.environ.get('MY_SECRET_KEY')
-    if api_key is None:
-      raise litellm.AuthenticationError("Api key is not provided")
+    def completion(self, messages: str, api_key: Optional[str] = None, **kwargs) -> litellm.ModelResponse:
+        api_key = api_key or os.environ.get("MY_SECRET_KEY")
+        if api_key is None:
+            raise litellm.AuthenticationError("`api_key` was not provided")
 
-    response = requests.post('https://www.my-fake-llm.ai/chat/completion', json={
-      'messages': messages
-    }, headers={'Authorization': api_key})
+        response = requests.post(
+            "https://www.my-custom-llm.ai/chat/completion",
+            json={"messages": messages},
+            headers={"Authorization": api_key},
+        )
 
-    return litellm.ModelResponse(**response.json())
+        return litellm.ModelResponse(**response.json())
 
+os.eviron["MY_SECRET_KEY"] = "" # "my-secret-key"
 
 my_custom_llm = MyCustomLLM()
 
 litellm.custom_provider_map = [  # 👈 KEY STEP - REGISTER HANDLER
-  {"provider": "my-custom-llm", "custom_handler": my_custom_llm}
+    {"provider": "my-custom-llm-endpoint", "custom_handler": my_custom_llm}
 ]
 
-api_key = os.environ['MY_SECRET_KEY']
+api_key = os.environ["MY_SECRET_KEY"]
 
-giskard.llm.set_llm_model("my-custom-llm/my-fake-llm-model", api_key=api_key)
+giskard.llm.set_llm_model("my-custom-llm-endpoint/my-custom-model", api_key=api_key)
 ```
 
 ::::::
 :::::::
 
-We are now ready to start.
-
 (model-wrapping)=
 
 ## Step 1: Wrap your model