update startup parameters

nerpaula · Simran-B · commit a4dd3b56176e · 2025-11-04T22:05:45.000+01:00
diff --git a/site/content/ai-suite/reference/importer.md b/site/content/ai-suite/reference/importer.md
@@ -98,62 +98,41 @@ To start the service, use the AI service endpoint `/v1/graphragimporter`.
 Please refer to the documentation of [AI service](gen-ai.md) for more
 information on how to use it.
 
-### Using Triton Inference Server (Private LLM)
-
-The first step is to install the LLM Host service with the LLM and
-embedding models of your choice. The setup will the use the 
-Triton Inference Server and MLflow at the backend. 
-For more details, please refer to the [Triton Inference Server](triton-inference-server.md)
-and [Mlflow](mlflow.md) documentation.
-
-Once the `llmhost` service is up-and-running, then you can start the Importer
-service using the below configuration:
-
-```json
-{
-  "env": {
-    "username": "your_username",
-    "db_name": "your_database_name",
-    "api_provider": "triton",
-    "triton_url": "your-arangodb-llm-host-url",
-    "triton_model": "mistral-nemo-instruct"
-  },
-}
-```
-
-Where:
-- `username`: ArangoDB database user with permissions to create and modify collections.
-- `db_name`: Name of the ArangoDB database where the knowledge graph will be stored.
-- `api_provider`: Specifies which LLM provider to use.
-- `triton_url`: URL of your Triton Inference Server instance. This should be the URL where your `llmhost` service is running.
-- `triton_model`: Name of the LLM model to use for text processing.
-
-### Using OpenAI (Public LLM)
+### Using OpenAI for chat and embedding
 
 ```json
 {
   "env": {
-    "openai_api_key": "your_openai_api_key",
     "username": "your_username",
     "db_name": "your_database_name",
-    "api_provider": "openai"
+    "chat_api_provider": "openai",
+    "chat_api_url": "https://api.openai.com/v1",
+    "embedding_api_url": "https://api.openai.com/v1",
+    "chat_model": "gpt-4o",
+    "embedding_model": "text-embedding-3-small",
+    "chat_api_key": "your_openai_api_key",
+    "embedding_api_key": "your_openai_api_key"
   },
 }
 ```
 
 Where:
 - `username`: ArangoDB database user with permissions to create and modify collections
 - `db_name`: Name of the ArangoDB database where the knowledge graph will be stored
-- `api_provider`: Specifies which LLM provider to use
-- `openai_api_key`: Your OpenAI API key
+- `chat_api_provider`: API provider for language model services
+- `embedding_api_url`: API endpoint URL for the embedding model service
+- `chat_model`: Specific language model to use for text generation and analysis
+- `embedding_model`: Specific model to use for generating text embeddings
+- `chat_api_key`: API key for authenticating with the chat/language model service
+- `embedding_api_key`: API key for authenticating with the embedding model service
 
 {{< info >}}
 By default, for OpenAI API, the service is using
 `gpt-4o-mini` and `text-embedding-3-small` models as LLM and
 embedding model respectively.
 {{< /info >}}
 
-### Using OpenRouter (Gemini, Anthropic, etc.)
+### Using OpenRouter for chat and OpenAI for embedding
 
 OpenRouter makes it possible to connect to a huge array of LLM API
 providers, including non-OpenAI LLMs like Gemini Flash, Anthropic Claude
@@ -167,27 +146,69 @@ while OpenAI is used for the embedding model.
       "env": {
         "db_name": "your_database_name",
         "username": "your_username",
-        "api_provider": "openrouter",
-        "openai_api_key": "your_openai_api_key",
-        "openrouter_api_key": "your_openrouter_api_key",
-        "openrouter_model": "mistralai/mistral-nemo"  // Specify a model here
+        "chat_api_provider": "openai",
+        "embedding_api_provider": "openai",
+        "chat_api_url": "https://openrouter.ai/api/v1",
+        "embedding_api_url": "https://api.openai.com/v1",
+        "chat_model": "mistral-nemo",
+        "embedding_model": "text-embedding-3-small",
+        "chat_api_key": "your_openrouter_api_key",
+        "embedding_api_key": "your_openai_api_key"
       },
     }
 ```
 
 Where:
-- `username`: ArangoDB database user with permissions to access collections  
-- `db_name`: Name of the ArangoDB database where the knowledge graph is stored  
-- `api_provider`: Specifies which LLM provider to use  
-- `openai_api_key`: Your OpenAI API key (for the embedding model)  
-- `openrouter_api_key`: Your OpenRouter API key (for the LLM)  
-- `openrouter_model`: Desired LLM (optional; default is `mistral-nemo`)
+- `username`: ArangoDB database user with permissions to access collections
+- `db_name`: Name of the ArangoDB database where the knowledge graph is stored
+- `chat_api_provider`: API provider for language model services
+- `embedding_api_url`: API endpoint URL for the embedding model service
+- `chat_model`: Specific language model to use for text generation and analysis
+- `embedding_model`: Specific model to use for generating text embeddings
+- `chat_api_key`: API key for authenticating with the chat/language model service
+- `embedding_api_key`: API key for authenticating with the embedding model service
 
 {{< info >}}
 When using OpenRouter, the service defaults to `mistral-nemo` for generation
 (via OpenRouter) and `text-embedding-3-small` for embeddings (via OpenAI).
 {{< /info >}}
 
+### Using Triton Inference Server for chat and embedding
+
+The first step is to install the LLM Host service with the LLM and
+embedding models of your choice. The setup will the use the 
+Triton Inference Server and MLflow at the backend. 
+For more details, please refer to the [Triton Inference Server](triton-inference-server.md)
+and [Mlflow](mlflow.md) documentation.
+
+Once the `llmhost` service is up-and-running, then you can start the Importer
+service using the below configuration:
+
+```json
+{
+  "env": {
+    "username": "your_username",
+    "db_name": "your_database_name",
+    "chat_api_provider": "triton",
+    "embedding_api_provider": "triton",
+    "chat_api_url": "your-arangodb-llm-host-url",
+    "embedding_api_url": "your-arangodb-llm-host-url",
+    "chat_model": "mistral-nemo-instruct",
+    "embedding_model": "nomic-embed-text-v1"
+  },
+}
+```
+
+Where:
+- `username`: ArangoDB database user with permissions to create and modify collections
+- `db_name`: Name of the ArangoDB database where the knowledge graph will be stored
+- `chat_api_provider`: Specifies which LLM provider to use for language model services
+- `embedding_api_provider`: API provider for embedding model services (e.g., "triton")
+- `chat_api_url`: API endpoint URL for the chat/language model service
+- `embedding_api_url`: API endpoint URL for the embedding model service
+- `chat_model`: Specific language model to use for text generation and analysis
+- `embedding_model`: Specific model to use for generating text embeddings
+
 ## Building Knowledge Graphs
 
 Once the service is installed successfully, you can follow these steps
diff --git a/site/content/ai-suite/reference/retriever.md b/site/content/ai-suite/reference/retriever.md
@@ -88,62 +88,42 @@ To start the service, use the AI service endpoint `/v1/graphragretriever`.
 Please refer to the documentation of [AI service](gen-ai.md) for more
 information on how to use it.
 
-### Using Triton Inference Server (Private LLM)
+### Using OpenAI for chat and embedding
 
-The first step is to install the LLM Host service with the LLM and
-embedding models of your choice. The setup will the use the 
-Triton Inference Server and MLflow at the backend. 
-For more details, please refer to the [Triton Inference Server](triton-inference-server.md)
-and [Mlflow](mlflow.md) documentation.
-
-Once the `llmhost` service is up-and-running, then you can start the Importer
-service using the below configuration:
 
 ```json
 {
   "env": {
     "username": "your_username",
     "db_name": "your_database_name",
-    "api_provider": "triton",
-    "triton_url": "your-arangodb-llm-host-url",
-    "triton_model": "mistral-nemo-instruct"
+    "chat_api_provider": "openai",
+    "chat_api_url": "https://api.openai.com/v1",
+    "embedding_api_url": "https://api.openai.com/v1",
+    "chat_model": "gpt-4o",
+    "embedding_model": "text-embedding-3-small",
+    "chat_api_key": "your_openai_api_key",
+    "embedding_api_key": "your_openai_api_key"
   },
 }
 ```
 
 Where:
-- `username`: ArangoDB database user with permissions to access collections.
-- `db_name`: Name of the ArangoDB database where the knowledge graph is stored.
-- `api_provider`: Specifies which LLM provider to use.
-- `triton_url`: URL of your Triton Inference Server instance. This should be the URL where your `llmhost` service is running.
-- `triton_model`: Name of the LLM model to use for text processing.
-
-### Using OpenAI (Public LLM)
-
-```json
-{
-  "env": {
-    "openai_api_key": "your_openai_api_key",
-    "username": "your_username",
-    "db_name": "your_database_name",
-    "api_provider": "openai"
-  },
-}
-```
-
-Where:
-- `username`: ArangoDB database user with permissions to access collections.
-- `db_name`: Name of the ArangoDB database where the knowledge graph is stored.
-- `api_provider`: Specifies which LLM provider to use.
-- `openai_api_key`: Your OpenAI API key.
+- `username`: ArangoDB database user with permissions to create and modify collections
+- `db_name`: Name of the ArangoDB database where the knowledge graph will be stored
+- `chat_api_provider`: API provider for language model services
+- `embedding_api_url`: API endpoint URL for the embedding model service
+- `chat_model`: Specific language model to use for text generation and analysis
+- `embedding_model`: Specific model to use for generating text embeddings
+- `chat_api_key`: API key for authenticating with the chat/language model service
+- `embedding_api_key`: API key for authenticating with the embedding model service
 
 {{< info >}}
 By default, for OpenAI API, the service is using
 `gpt-4o-mini` and `text-embedding-3-small` models as LLM and
 embedding model respectively.
 {{< /info >}}
 
-### Using OpenRouter (Gemini, Anthropic, etc.)
+### Using OpenRouter for chat and OpenAI for embedding
 
 OpenRouter makes it possible to connect to a huge array of LLM API providers,
 including non-OpenAI LLMs like Gemini Flash, Anthropic Claude and publicly hosted
@@ -157,27 +137,69 @@ OpenAI is used for the embedding model.
       "env": {
         "db_name": "your_database_name",
         "username": "your_username",
-        "api_provider": "openrouter",
-        "openai_api_key": "your_openai_api_key",
-        "openrouter_api_key": "your_openrouter_api_key",
-        "openrouter_model": "mistralai/mistral-nemo"  // Specify a model here
+        "chat_api_provider": "openai",
+        "embedding_api_provider": "openai",
+        "chat_api_url": "https://openrouter.ai/api/v1",
+        "embedding_api_url": "https://api.openai.com/v1",
+        "chat_model": "mistral-nemo",
+        "embedding_model": "text-embedding-3-small",
+        "chat_api_key": "your_openrouter_api_key",
+        "embedding_api_key": "your_openai_api_key"
       },
     }
 ```
 
 Where:
-- `username`: ArangoDB database user with permissions to access collections.
-- `db_name`: Name of the ArangoDB database where the knowledge graph is stored.
-- `api_provider`: Specifies which LLM provider to use.
-- `openai_api_key`: Your OpenAI API key (for the embedding model).
-- `openrouter_api_key`: Your OpenRouter API key (for the LLM).
-- `openrouter_model`: Desired LLM (optional; default is `mistral-nemo`).
+- `username`: ArangoDB database user with permissions to access collections
+- `db_name`: Name of the ArangoDB database where the knowledge graph is stored
+- `chat_api_provider`: API provider for language model services
+- `embedding_api_url`: API endpoint URL for the embedding model service
+- `chat_model`: Specific language model to use for text generation and analysis
+- `embedding_model`: Specific model to use for generating text embeddings
+- `chat_api_key`: API key for authenticating with the chat/language model service
+- `embedding_api_key`: API key for authenticating with the embedding model service
 
 {{< info >}}
 When using OpenRouter, the service defaults to `mistral-nemo` for generation
 (via OpenRouter) and `text-embedding-3-small` for embeddings (via OpenAI).
 {{< /info >}}
 
+### Using Triton Inference Server for chat and embedding
+
+The first step is to install the LLM Host service with the LLM and
+embedding models of your choice. The setup will the use the 
+Triton Inference Server and MLflow at the backend. 
+For more details, please refer to the [Triton Inference Server](triton-inference-server.md)
+and [Mlflow](mlflow.md) documentation.
+
+Once the `llmhost` service is up-and-running, then you can start the Importer
+service using the below configuration:
+
+```json
+{
+  "env": {
+    "username": "your_username",
+    "db_name": "your_database_name",
+    "chat_api_provider": "triton",
+    "embedding_api_provider": "triton",
+    "chat_api_url": "your-arangodb-llm-host-url",
+    "embedding_api_url": "your-arangodb-llm-host-url",
+    "chat_model": "mistral-nemo-instruct",
+    "embedding_model": "nomic-embed-text-v1"
+  },
+}
+```
+
+Where:
+- `username`: ArangoDB database user with permissions to create and modify collections
+- `db_name`: Name of the ArangoDB database where the knowledge graph will be stored
+- `chat_api_provider`: Specifies which LLM provider to use for language model services
+- `embedding_api_provider`: API provider for embedding model services (e.g., "triton")
+- `chat_api_url`: API endpoint URL for the chat/language model service
+- `embedding_api_url`: API endpoint URL for the embedding model service
+- `chat_model`: Specific language model to use for text generation and analysis
+- `embedding_model`: Specific model to use for generating text embeddings
+
 ## Executing queries
 
 After the Retriever service is installed successfully, you can interact with 
diff --git a/site/content/ai-suite/reference/triton-inference-server.md b/site/content/ai-suite/reference/triton-inference-server.md
@@ -26,8 +26,8 @@ following steps:
 
 1. Install the Triton LLM Host service.
 2. Register your LLM model to MLflow by uploading the required files.
-3. Configure the [Importer](importer.md#using-triton-inference-server-private-llm) service to use your LLM model.
-4. Configure the [Retriever](retriever.md#using-triton-inference-server-private-llm) service to use your LLM model.
+3. Configure the [Importer](importer.md#using-triton-inference-server-for-chat-and-embedding) service to use your LLM model.
+4. Configure the [Retriever](retriever.md#using-triton-inference-server-for-chat-and-embedding) service to use your LLM model.
 
 {{< tip >}}
 Check out the dedicated [ArangoDB MLflow](mlflow.md) documentation page to learn
