From a4dd3b56176ea7df15992f64feef894835ededb0 Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Fri, 17 Oct 2025 13:50:08 +0200
Subject: [PATCH 01/10] update startup parameters

---
 site/content/ai-suite/reference/importer.md   | 113 ++++++++++-------
 site/content/ai-suite/reference/retriever.md  | 116 +++++++++++-------
 .../reference/triton-inference-server.md      |   4 +-
 3 files changed, 138 insertions(+), 95 deletions(-)

diff --git a/site/content/ai-suite/reference/importer.md b/site/content/ai-suite/reference/importer.md
index e4cce5d200..834f2a83c1 100644
--- a/site/content/ai-suite/reference/importer.md
+++ b/site/content/ai-suite/reference/importer.md
@@ -98,45 +98,20 @@ 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"
   },
 }
 ```
@@ -144,8 +119,12 @@ Where:
 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
@@ -153,7 +132,7 @@ By default, for OpenAI API, the service is using
 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
index 5949d8a369..c43f7931c4 100644
--- a/site/content/ai-suite/reference/retriever.md
+++ b/site/content/ai-suite/reference/retriever.md
@@ -88,54 +88,34 @@ 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
@@ -143,7 +123,7 @@ By default, for OpenAI API, the service is using
 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
index 458226743e..1e1b982932 100644
--- 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

From a32d84104fdf3788112d78d2dbebb891e87e767e Mon Sep 17 00:00:00 2001
From: Paula Mihu <97217318+nerpaula@users.noreply.github.com>
Date: Wed, 22 Oct 2025 12:36:10 +0200
Subject: [PATCH 02/10] Apply suggestions from code review

Co-authored-by: Anthony Mahanna <43019056+aMahanna@users.noreply.github.com>
---
 site/content/ai-suite/reference/importer.md  | 3 +++
 site/content/ai-suite/reference/retriever.md | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/site/content/ai-suite/reference/importer.md b/site/content/ai-suite/reference/importer.md
index 834f2a83c1..e19b04d235 100644
--- a/site/content/ai-suite/reference/importer.md
+++ b/site/content/ai-suite/reference/importer.md
@@ -107,6 +107,7 @@ information on how to use it.
     "db_name": "your_database_name",
     "chat_api_provider": "openai",
     "chat_api_url": "https://api.openai.com/v1",
+    "embedding_api_provider": "openai",
     "embedding_api_url": "https://api.openai.com/v1",
     "chat_model": "gpt-4o",
     "embedding_model": "text-embedding-3-small",
@@ -120,6 +121,7 @@ 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`: API provider for language model services
+- `embeddinga_api_provider`: API provider for embedding 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
@@ -162,6 +164,7 @@ Where:
 - `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_provider`: API provider for embedding 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
diff --git a/site/content/ai-suite/reference/retriever.md b/site/content/ai-suite/reference/retriever.md
index c43f7931c4..6a688dad94 100644
--- a/site/content/ai-suite/reference/retriever.md
+++ b/site/content/ai-suite/reference/retriever.md
@@ -98,6 +98,7 @@ information on how to use it.
     "db_name": "your_database_name",
     "chat_api_provider": "openai",
     "chat_api_url": "https://api.openai.com/v1",
+    "embedding_api_provider": "openai",
     "embedding_api_url": "https://api.openai.com/v1",
     "chat_model": "gpt-4o",
     "embedding_model": "text-embedding-3-small",
@@ -111,6 +112,7 @@ 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`: API provider for language model services
+- `emebdding_api_provider`: API provider for embedding 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
@@ -153,6 +155,7 @@ Where:
 - `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_provider`: API provider for embedding 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

From 02adb5068c9605e77fcfe61e8b363f107f9c2343 Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Wed, 22 Oct 2025 15:56:00 +0200
Subject: [PATCH 03/10] apply changes to all versions, fix typo

---
 site/content/ai-suite/reference/importer.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/site/content/ai-suite/reference/importer.md b/site/content/ai-suite/reference/importer.md
index e19b04d235..1e534320df 100644
--- a/site/content/ai-suite/reference/importer.md
+++ b/site/content/ai-suite/reference/importer.md
@@ -121,7 +121,7 @@ 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`: API provider for language model services
-- `embeddinga_api_provider`: API provider for embedding model services
+- `embedding_api_provider`: API provider for embedding 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

From 3504267b99cf7d9ab9304b5da13e19d6ba52168d Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Fri, 24 Oct 2025 12:34:58 +0200
Subject: [PATCH 04/10] instant and deep search

---
 site/content/ai-suite/reference/retriever.md | 59 +++++++++-----------
 1 file changed, 27 insertions(+), 32 deletions(-)

diff --git a/site/content/ai-suite/reference/retriever.md b/site/content/ai-suite/reference/retriever.md
index 6a688dad94..d0e3aba1e1 100644
--- a/site/content/ai-suite/reference/retriever.md
+++ b/site/content/ai-suite/reference/retriever.md
@@ -15,9 +15,9 @@ the Arango team.
 ## Overview
 
 The Retriever service offers two distinct search methods:
-- **Global search**: Analyzes entire document to identify themes and patterns,
+- **Instant search**: Analyzes entire document to identify themes and patterns,
   perfect for high-level insights and comprehensive summaries.
-- **Local search**: Focuses on specific entities and their relationships, ideal
+- **Deep search**: Focuses on specific entities and their relationships, ideal
   for detailed queries about particular concepts.
 
 The service supports both private (Triton Inference Server) and public (OpenAI)
@@ -33,19 +33,19 @@ graph and get contextually relevant responses.
 - Configurable community hierarchy levels
 
 {{< tip >}}
-You can also use the GraphRAG Retriever service via the ArangoDB [web interface](../graphrag/web-interface.md).
+You can also use the GraphRAG Retriever service via the [web interface](../graphrag/web-interface.md).
 {{< /tip >}}
 
 ## Search methods
 
 The Retriever service enables intelligent search and retrieval of information
-from your knowledge graph. It provides two powerful search methods, global Search
-and local Search, that leverage the structured knowledge graph created by the Importer
+from your knowledge graph. It provides two powerful search methods, instant search
+and deep search, that leverage the structured knowledge graph created by the Importer
 to deliver accurate and contextually relevant responses to your natural language queries.
 
-### Global search
+### Deep search
 
-Global search is designed for queries that require understanding and aggregation
+Deep search is designed for queries that require understanding and aggregation
 of information across your entire document. It's particularly effective for questions
 about overall themes, patterns, or high-level insights in your data.
 
@@ -60,9 +60,9 @@ about overall themes, patterns, or high-level insights in your data.
 - "Summarize the key findings across all documents"
 - "What are the most important concepts discussed?"
 
-### Local search
+### Instant search
 
-Local search focuses on specific entities and their relationships within your
+Instant search focuses on specific entities and their relationships within your
 knowledge graph. It is ideal for detailed queries about particular concepts,
 entities, or relationships.
 
@@ -210,28 +210,32 @@ it using the following HTTP endpoints, based on the selected search method.
 
 {{< tabs "executing-queries" >}}
 
-{{< tab "Local search" >}}
+{{< tab "Instant search" >}}
 ```bash
-curl -X POST /v1/graphrag-query \
+curl -X POST /v1/graphrag-query-stream \
   -H "Content-Type: application/json" \
   -d '{
     "query": "What is the AR3 Drone?",
-    "query_type": 2,
-    "provider": 0
+    "query_type": "UNIFIED",
+    "provider": 0,
+    "include_metadata": true,
+    "use_llm_planner": false
   }'
 ```
 {{< /tab >}}
 
-{{< tab "Global search" >}}
+{{< tab "Deep search" >}}
 
 ```bash
 curl -X POST /v1/graphrag-query \
   -H "Content-Type: application/json" \
   -d '{
-    "query": "What is the AR3 Drone?",
+    "query": "What are the main themes and topics discussed in the documents?",
     "level": 1,
-    "query_type": 1,
-    "provider": 0
+    "query_type": "LOCAL",
+    "provider": 0,
+    "include_metadata": true,
+    "use_llm_planner": true
   }'
 ```
 {{< /tab >}}
@@ -240,13 +244,15 @@ curl -X POST /v1/graphrag-query \
 
 The request parameters are the following:
 - `query`: Your search query text.
-- `level`: The community hierarchy level to use for the search (`1` for top-level communities).
+- `level`: The community hierarchy level to use for the search (`1` for top-level communities). Defaults to `2` if not provided.
 - `query_type`: The type of search to perform.
-  - `1`: Global search.
-  - `2`: Local search.
-- `provider`: The LLM provider to use
+  - `UNIFIED`: Instant search.
+  - `LOCAL`: Deep search.
+- `provider`: The LLM provider to use:
   - `0`: OpenAI (or OpenRouter)
   - `1`: Triton
+- `include_metadata`: Whether to include metadata in the response. If not specified, defaults to `true`.
+- `use_llm_planner`: Whether to use the LLM planner for intelligent query processing. If not specified, defaults to `true`.
 
 ## Health check
 
@@ -274,17 +280,6 @@ properties:
 }
 ```
 
-## Best Practices
-
-- **Choose the right search method**:
-   - Use global search for broad, thematic queries.
-   - Use local search for specific entity or relationship queries.
-
-
-- **Performance considerations**:
-   - Global search may take longer due to its map-reduce process.
-   - Local search is typically faster for concrete queries.
-
 ## API Reference
 
 For detailed API documentation, see the

From bec2f30e9d40e15d134808102808dafe2509c922 Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Fri, 24 Oct 2025 13:55:47 +0200
Subject: [PATCH 05/10] update description of instant and deep search; fix some
 anchor links

---
 .../ai-suite/graphrag/web-interface.md        |  8 ++---
 site/content/ai-suite/reference/retriever.md  | 30 +++++++++++--------
 2 files changed, 21 insertions(+), 17 deletions(-)

diff --git a/site/content/ai-suite/graphrag/web-interface.md b/site/content/ai-suite/graphrag/web-interface.md
index 7438127d6f..6caa04b177 100644
--- a/site/content/ai-suite/graphrag/web-interface.md
+++ b/site/content/ai-suite/graphrag/web-interface.md
@@ -159,10 +159,10 @@ See also the [GraphRAG Retriever](../reference/retriever.md) documentation.
 ## Chat with your Knowledge Graph
 
 The Retriever service provides two search methods:
-- [Local search](../reference/retriever.md#local-search): Local queries let you
-  explore specific nodes and their direct connections.
-- [Global search](../reference/retriever.md#global-search): Global queries uncover
-  broader patters and relationships across the entire Knowledge Graph.
+- [Instant search](../reference/retriever.md#instant-search): Instant
+  queries provide fast responses.
+- [Deep search](../reference/retriever.md#deep-search): This option will take
+  longer to return a response.
 
 ![Chat with your Knowledge Graph](../../images/graphrag-ui-chat.png)
 
diff --git a/site/content/ai-suite/reference/retriever.md b/site/content/ai-suite/reference/retriever.md
index d0e3aba1e1..1a6e324346 100644
--- a/site/content/ai-suite/reference/retriever.md
+++ b/site/content/ai-suite/reference/retriever.md
@@ -15,10 +15,10 @@ the Arango team.
 ## Overview
 
 The Retriever service offers two distinct search methods:
-- **Instant search**: Analyzes entire document to identify themes and patterns,
-  perfect for high-level insights and comprehensive summaries.
-- **Deep search**: Focuses on specific entities and their relationships, ideal
-  for detailed queries about particular concepts.
+- **Instant search**: Focuses on specific entities and their relationships, ideal
+  for fast queries about particular concepts.
+- **Deep search**: Analyzes the knowledge graph structure to identify themes and patterns,
+  perfect for comprehensive insights and detailed summaries.
 
 The service supports both private (Triton Inference Server) and public (OpenAI)
 LLM deployments, making it flexible for various security and infrastructure
@@ -43,14 +43,17 @@ from your knowledge graph. It provides two powerful search methods, instant sear
 and deep search, that leverage the structured knowledge graph created by the Importer
 to deliver accurate and contextually relevant responses to your natural language queries.
 
-### Deep search
+### Deep Search
 
-Deep search is designed for queries that require understanding and aggregation
-of information across your entire document. It's particularly effective for questions
-about overall themes, patterns, or high-level insights in your data.
+Deep Search is designed for highly detailed, accurate responses that require understanding
+what kind of information is available in different parts of the knowledge graph and
+sequentially retrieving information in an LLM-guided research process. Use whenever
+detail and accuracy are required (e.g. aggregation of highly technical details) and
+very short latency is not (i.e. caching responses for frequently asked questions,
+or use case with agents or research use cases).
 
 - **Community-Based Analysis**: Uses pre-generated community reports from your
-  knowledge graph to understand the overall structure and themes of your data,
+  knowledge graph to understand the overall structure and themes of your data.
 - **Map-Reduce Processing**:
    - **Map Stage**: Processes community reports in parallel, generating intermediate responses with rated points.
    - **Reduce Stage**: Aggregates the most important points to create a comprehensive final response.
@@ -60,11 +63,12 @@ about overall themes, patterns, or high-level insights in your data.
 - "Summarize the key findings across all documents"
 - "What are the most important concepts discussed?"
 
-### Instant search
+### Instant Search
 
-Instant search focuses on specific entities and their relationships within your
-knowledge graph. It is ideal for detailed queries about particular concepts,
-entities, or relationships.
+Instant Search is designed for responses with very short latency. It triggers
+fast unified retrieval over relevant parts of the knowledge graph via hybrid
+(semantic and lexical) search and graph expansion algorithms, producing a fast,
+streamed natural-language response with clickable references to the relevant documents.
 
 - **Entity Identification**: Identifies relevant entities from the knowledge graph based on the query.
 - **Context Gathering**: Collects:

From b33024b54b6ac460a28390819b6b2dc386bb4ae0 Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Fri, 31 Oct 2025 15:53:23 +0100
Subject: [PATCH 06/10] add missing description for chat_api_url

---
 site/content/ai-suite/reference/importer.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/site/content/ai-suite/reference/importer.md b/site/content/ai-suite/reference/importer.md
index 1e534320df..e0eaa718ab 100644
--- a/site/content/ai-suite/reference/importer.md
+++ b/site/content/ai-suite/reference/importer.md
@@ -121,6 +121,7 @@ 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`: API provider for language model services
+- `chat_api_url`: API endpoint URL for the chat/language model service
 - `embedding_api_provider`: API provider for embedding 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
@@ -164,6 +165,7 @@ Where:
 - `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
+- `chat_api_url`: API endpoint URL for the chat/language model service
 - `embedding_api_provider`: API provider for embedding 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

From e48a6d062ce079824af92b9b77a53433f723a633 Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Fri, 31 Oct 2025 15:56:14 +0100
Subject: [PATCH 07/10] fix typo

---
 site/content/ai-suite/reference/retriever.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/site/content/ai-suite/reference/retriever.md b/site/content/ai-suite/reference/retriever.md
index 1a6e324346..947fc1bb98 100644
--- a/site/content/ai-suite/reference/retriever.md
+++ b/site/content/ai-suite/reference/retriever.md
@@ -116,7 +116,8 @@ 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`: API provider for language model services
-- `emebdding_api_provider`: API provider for embedding model services
+- `chat_api_url`: API endpoint URL for the chat/language model service
+- `embedding_api_provider`: API provider for embedding 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

From 81d49fb368dcbbfd27e5ded9e133cd44f2159d89 Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Mon, 3 Nov 2025 17:01:14 +0100
Subject: [PATCH 08/10] reference project creation in retriever file; add
 project name validation rules

---
 site/content/ai-suite/reference/importer.md  | 14 ++++++++++++++
 site/content/ai-suite/reference/retriever.md |  9 +++++++++
 2 files changed, 23 insertions(+)

diff --git a/site/content/ai-suite/reference/importer.md b/site/content/ai-suite/reference/importer.md
index e0eaa718ab..4a36403cdb 100644
--- a/site/content/ai-suite/reference/importer.md
+++ b/site/content/ai-suite/reference/importer.md
@@ -35,6 +35,10 @@ To create a new GraphRAG project, use the `CreateProject` method by sending a
 `project_name` and a `project_type` in the request body. Optionally, you can
 provide a `project_description`.
 
+The `project_name` must follow these validation rules:
+- Length: 1–63 characters
+- Allowed characters: letters, numbers, underscores (`_`), and hyphens (`-`)
+
 ```curl
 curl -X POST "https://<ExternalEndpoint>:8529/ai/v1/project" \
 -H "Content-Type: application/json" \
@@ -44,6 +48,7 @@ curl -X POST "https://<ExternalEndpoint>:8529/ai/v1/project" \
   "project_description": "A documentation project for GraphRAG."
 }'
 ```
+
 All the relevant ArangoDB collections (such as documents, chunks, entities,
 relationships, and communities) created during the import process will
 have the project name as a prefix. For example, the Documents collection will
@@ -51,6 +56,15 @@ become `<project_name>_Documents`. The Knowledge Graph will also use the project
 name as a prefix. If no project name is specified, then all collections
 are prefixed with `default_project`, e.g., `default_project_Documents`.
 
+Once created, you can reference your project in other services (such as the 
+Importer or Retriever) using the `genai_project_name` field:
+
+```json
+{
+  "genai_project_name": "docs"
+}
+```
+
 ### Project metadata
 
 Additional project metadata is accessible via the following endpoint, replacing
diff --git a/site/content/ai-suite/reference/retriever.md b/site/content/ai-suite/reference/retriever.md
index 947fc1bb98..2571520000 100644
--- a/site/content/ai-suite/reference/retriever.md
+++ b/site/content/ai-suite/reference/retriever.md
@@ -36,6 +36,15 @@ graph and get contextually relevant responses.
 You can also use the GraphRAG Retriever service via the [web interface](../graphrag/web-interface.md).
 {{< /tip >}}
 
+## Prerequisites
+
+Before using the Retriever service, you need to create a GraphRAG project and 
+import data using the Importer service.
+
+For detailed instructions on creating a project, see 
+[Creating a new project](importer.md#creating-a-new-project) in the Importer 
+documentation.
+
 ## Search methods
 
 The Retriever service enables intelligent search and retrieval of information

From e657d19b4bfd4868d0854ee366bee9bed5fce5b9 Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Tue, 4 Nov 2025 16:04:43 +0100
Subject: [PATCH 09/10] remove username from startup parameters

---
 site/content/ai-suite/reference/importer.md  | 6 ------
 site/content/ai-suite/reference/retriever.md | 6 ------
 2 files changed, 12 deletions(-)

diff --git a/site/content/ai-suite/reference/importer.md b/site/content/ai-suite/reference/importer.md
index 4a36403cdb..1e83f8a475 100644
--- a/site/content/ai-suite/reference/importer.md
+++ b/site/content/ai-suite/reference/importer.md
@@ -117,7 +117,6 @@ information on how to use it.
 ```json
 {
   "env": {
-    "username": "your_username",
     "db_name": "your_database_name",
     "chat_api_provider": "openai",
     "chat_api_url": "https://api.openai.com/v1",
@@ -132,7 +131,6 @@ information on how to use it.
 ```
 
 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`: API provider for language model services
 - `chat_api_url`: API endpoint URL for the chat/language model service
@@ -162,7 +160,6 @@ while OpenAI is used for the embedding model.
     {
       "env": {
         "db_name": "your_database_name",
-        "username": "your_username",
         "chat_api_provider": "openai",
         "embedding_api_provider": "openai",
         "chat_api_url": "https://openrouter.ai/api/v1",
@@ -176,7 +173,6 @@ while OpenAI is used for the embedding model.
 ```
 
 Where:
-- `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
 - `chat_api_url`: API endpoint URL for the chat/language model service
@@ -206,7 +202,6 @@ service using the below configuration:
 ```json
 {
   "env": {
-    "username": "your_username",
     "db_name": "your_database_name",
     "chat_api_provider": "triton",
     "embedding_api_provider": "triton",
@@ -219,7 +214,6 @@ service using the below configuration:
 ```
 
 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")
diff --git a/site/content/ai-suite/reference/retriever.md b/site/content/ai-suite/reference/retriever.md
index 2571520000..5ce96d128d 100644
--- a/site/content/ai-suite/reference/retriever.md
+++ b/site/content/ai-suite/reference/retriever.md
@@ -107,7 +107,6 @@ information on how to use it.
 ```json
 {
   "env": {
-    "username": "your_username",
     "db_name": "your_database_name",
     "chat_api_provider": "openai",
     "chat_api_url": "https://api.openai.com/v1",
@@ -122,7 +121,6 @@ information on how to use it.
 ```
 
 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`: API provider for language model services
 - `chat_api_url`: API endpoint URL for the chat/language model service
@@ -152,7 +150,6 @@ OpenAI is used for the embedding model.
     {
       "env": {
         "db_name": "your_database_name",
-        "username": "your_username",
         "chat_api_provider": "openai",
         "embedding_api_provider": "openai",
         "chat_api_url": "https://openrouter.ai/api/v1",
@@ -166,7 +163,6 @@ OpenAI is used for the embedding model.
 ```
 
 Where:
-- `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_provider`: API provider for embedding model services
@@ -195,7 +191,6 @@ service using the below configuration:
 ```json
 {
   "env": {
-    "username": "your_username",
     "db_name": "your_database_name",
     "chat_api_provider": "triton",
     "embedding_api_provider": "triton",
@@ -208,7 +203,6 @@ service using the below configuration:
 ```
 
 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")

From 52176cea0cee6bee77f62b45aa4bbc0d3f7c371e Mon Sep 17 00:00:00 2001
From: Paula <paula.mihu@arangodb.com>
Date: Tue, 4 Nov 2025 17:35:27 +0100
Subject: [PATCH 10/10] update startup parameters in genai service; add
 project_db_name in project creation; move and extend Projects

---
 site/content/ai-suite/reference/gen-ai.md    | 154 +++++++++++++------
 site/content/ai-suite/reference/importer.md  |  53 +------
 site/content/ai-suite/reference/retriever.md |  12 +-
 3 files changed, 125 insertions(+), 94 deletions(-)

diff --git a/site/content/ai-suite/reference/gen-ai.md b/site/content/ai-suite/reference/gen-ai.md
index f545a7e255..078b3037db 100644
--- a/site/content/ai-suite/reference/gen-ai.md
+++ b/site/content/ai-suite/reference/gen-ai.md
@@ -33,22 +33,15 @@ in the platform. All services support the `profiles` field, which you can use
 to define the profile to use for the service. For example, you can define a
 GPU profile that enables the service to run an LLM on GPU resources.
 
-## LLM Host Service Creation Request Body
+## Service Creation Request Body
 
-```json
-{
-    "env": {
-        "model_name": "<registered_model_name>"
-    }
-}
-```
-
-## Using Labels in Creation Request Body
+The following example shows a complete request body with all available options:
 
 ```json
 {
     "env": {
-        "model_name": "<registered_model_name>"
+        "model_name": "<registered_model_name>",
+        "profiles": "gpu,internal"
     },
     "labels": {
         "key1": "value1",
@@ -57,32 +50,116 @@ GPU profile that enables the service to run an LLM on GPU resources.
 }
 ```
 
-{{< info >}}
-Labels are optional. Labels can be used to filter and identify services in
-the Platform. If you want to use labels, define them as a key-value pair in `labels`
-within the `env` field.
-{{< /info >}}
+**Optional fields:**
+
+- **labels**: Key-value pairs used to filter and identify services in the platform.
+- **profiles**: A comma-separated string defining which profiles to use for the 
+  service (e.g., `"gpu,internal"`). If not set, the service is created with the 
+  default profile. Profiles must be present and created in the platform before 
+  they can be used.
+
+The parameters required for the deployment of each service are defined in the
+corresponding service documentation. See [Importer](importer.md)
+and [Retriever](retriever.md).
+
+## Projects
+
+Projects help you organize your GraphRAG work by grouping related services and 
+keeping your data separate. When the Importer service creates ArangoDB collections 
+(such as documents, chunks, entities, relationships, and communities), it uses 
+your project name as a prefix. For example, a project named `docs` will have 
+collections like `docs_Documents`, `docs_Chunks`, and so on.
 
-## Using Profiles in Creation Request Body
+### Creating a project
+
+To create a new GraphRAG project, send a POST request to the project endpoint:
+
+```bash
+curl -X POST "https://<ExternalEndpoint>:8529/gen-ai/v1/project" \
+  -H "Authorization: Bearer <your-bearer-token>" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "project_name": "docs",
+    "project_type": "graphrag",
+    "project_db_name": "documentation",
+    "project_description": "A documentation project for GraphRAG."
+  }'
+```
+
+Where:
+- **project_name** (required): Unique identifier for your project. Must be 1-63 
+  characters and contain only letters, numbers, underscores (`_`), and hyphens (`-`).
+- **project_type** (required): Type of project (e.g., `"graphrag"`).
+- **project_db_name** (required): The ArangoDB database name where the project 
+  will be created.
+- **project_description** (optional): A description of your project.
+
+Once created, you can reference your project in service deployments using the 
+`genai_project_name` field:
 
 ```json
 {
-    "env": {
-        "model_name": "<registered_model_name>",
-        "profiles": "gpu,internal"
-    }
+  "env": {
+    "genai_project_name": "docs"
+  }
 }
 ```
 
-{{< info >}}
-The `profiles` field is optional. If it is not set, the service is created with
-the default profile. Profiles must be present and created in the Platform before
-they can be used. If you want to use profiles, define them as a comma-separated
-string in `profiles` within the `env` field.
-{{< /info >}}
+### Listing projects
 
-The parameters required for the deployment of each service are defined in the
-corresponding service documentation.
+**List all project names in a database:**
+
+```bash
+curl -X GET "https://<ExternalEndpoint>:8529/gen-ai/v1/all_project_names/<database_name>" \
+  -H "Authorization: Bearer <your-bearer-token>"
+```
+
+This returns only the project names for quick reference.
+
+**List all projects with full metadata in a database:**
+
+```bash
+curl -X GET "https://<ExternalEndpoint>:8529/gen-ai/v1/all_projects/<database_name>" \
+  -H "Authorization: Bearer <your-bearer-token>"
+```
+
+This returns complete project objects including metadata, associated services, 
+and knowledge graph information.
+
+### Getting project details
+
+Retrieve comprehensive metadata for a specific project:
+
+```bash
+curl -X GET "https://<ExternalEndpoint>:8529/gen-ai/v1/project_by_name/<database_name>/<project_name>" \
+  -H "Authorization: Bearer <your-bearer-token>"
+```
+
+The response includes:
+- Project configuration
+- Associated Importer and Retriever services
+- Knowledge graph metadata
+- Service status information
+- Last modification timestamp
+
+### Deleting a project
+
+Remove a project's metadata from the GenAI service:
+
+```bash
+curl -X DELETE "https://<ExternalEndpoint>:8529/gen-ai/v1/project/<database_name>/<project_name>" \
+  -H "Authorization: Bearer <your-bearer-token>"
+```
+
+{{< warning >}}
+Deleting a project only removes the project metadata from the GenAI service. 
+It does **not** delete:
+- Services associated with the project (must be deleted separately)
+- ArangoDB collections and data
+- Knowledge graphs
+
+You must manually delete services and collections if needed.
+{{< /warning >}}
 
 ## Obtaining a Bearer Token
 
@@ -101,7 +178,7 @@ documentation.
 
 ## Complete Service lifecycle example
 
-The example below shows how to install, monitor, and uninstall the Importer service.
+The example below shows how to install, monitor, and uninstall the [Importer](importer.md) service.
 
 ### Step 1: Installing the service
 
@@ -111,11 +188,10 @@ curl -X POST https://<ExternalEndpoint>:8529/ai/v1/graphragimporter \
   -H "Content-Type: application/json" \
   -d '{
     "env": {
-      "username": "<your-username>",
       "db_name": "<your-database-name>",
-      "api_provider": "<your-api-provider>",
-      "triton_url": "<your-arangodb-llm-host-url>",
-      "triton_model": "<your-triton-model>"
+      "chat_api_provider": "<your-api-provider>",
+      "chat_api_key": "<your-llm-provider-api-key>",
+      "chat_model": "<model-name>"
     }
   }'
 ```
@@ -176,16 +252,6 @@ curl -X DELETE https://<ExternalEndpoint>:8529/ai/v1/service/arangodb-graphrag-i
 - **Authentication**: All requests use the same Bearer token in the `Authorization` header
 {{< /info >}}
 
-### Customizing the example
-
-Replace the following values with your actual configuration:
-- `<your-username>` - Your database username.
-- `<your-database-name>` - Target database name.
-- `<your-api-provider>` - Your API provider (e.g., `triton`)
-- `<your-arangodb-llm-host-url>` - Your LLM host service URL.
-- `<your-triton-model>` - Your Triton model name (e.g., `mistral-nemo-instruct`).
-- `<your-bearer-token>` - Your authentication token.
-
 ## Service configuration
 
 The AI orchestrator service is **started by default**. 
diff --git a/site/content/ai-suite/reference/importer.md b/site/content/ai-suite/reference/importer.md
index 1e83f8a475..5f66ecbe3e 100644
--- a/site/content/ai-suite/reference/importer.md
+++ b/site/content/ai-suite/reference/importer.md
@@ -28,54 +28,17 @@ different concepts in your document with the Retriever service.
 You can also use the GraphRAG Importer service via the [Data Platform web interface](../graphrag/web-interface.md).
 {{< /tip >}}
 
-## Creating a new project
-
-To create a new GraphRAG project, use the `CreateProject` method by sending a
-`POST` request to the `/ai/v1/project` endpoint. You must provide a unique
-`project_name` and a `project_type` in the request body. Optionally, you can
-provide a `project_description`.
-
-The `project_name` must follow these validation rules:
-- Length: 1–63 characters
-- Allowed characters: letters, numbers, underscores (`_`), and hyphens (`-`)
-
-```curl
-curl -X POST "https://<ExternalEndpoint>:8529/ai/v1/project" \
--H "Content-Type: application/json" \
--d '{
-  "project_name": "docs",
-  "project_type": "graphrag",
-  "project_description": "A documentation project for GraphRAG."
-}'
-```
-
-All the relevant ArangoDB collections (such as documents, chunks, entities,
-relationships, and communities) created during the import process will
-have the project name as a prefix. For example, the Documents collection will
-become `<project_name>_Documents`. The Knowledge Graph will also use the project
-name as a prefix. If no project name is specified, then all collections
-are prefixed with `default_project`, e.g., `default_project_Documents`.
-
-Once created, you can reference your project in other services (such as the 
-Importer or Retriever) using the `genai_project_name` field:
-
-```json
-{
-  "genai_project_name": "docs"
-}
-```
-
-### Project metadata
+## Prerequisites
 
-Additional project metadata is accessible via the following endpoint, replacing
-`<your_project>` with the actual name of your project:
+Before importing data, you need to create a GraphRAG project. Projects help you 
+organize your work and keep your data separate from other projects.
 
-```
-GET /ai/v1/project_by_name/<your_project>
-```
+For detailed instructions on creating and managing projects, see the 
+[Projects](gen-ai.md#projects) section in the GenAI Orchestration Service 
+documentation.
 
-The endpoint provides comprehensive metadata about your project's components,
-including its importer and retriever services and their status.
+Once you have created a project, you can reference it when deploying the Importer 
+service using the `genai_project_name` field in the service configuration.
 
 ## Deployment options
 
diff --git a/site/content/ai-suite/reference/retriever.md b/site/content/ai-suite/reference/retriever.md
index 5ce96d128d..8eb9d64711 100644
--- a/site/content/ai-suite/reference/retriever.md
+++ b/site/content/ai-suite/reference/retriever.md
@@ -38,12 +38,14 @@ You can also use the GraphRAG Retriever service via the [web interface](../graph
 
 ## Prerequisites
 
-Before using the Retriever service, you need to create a GraphRAG project and 
-import data using the Importer service.
+Before using the Retriever service, you need to:
 
-For detailed instructions on creating a project, see 
-[Creating a new project](importer.md#creating-a-new-project) in the Importer 
-documentation.
+1. **Create a GraphRAG project** - For detailed instructions on creating and 
+   managing projects, see the [Projects](gen-ai.md#projects) section in the 
+   GenAI Orchestration Service documentation.
+
+2. **Import data** - Use the [Importer](importer.md) service to transform your 
+   text documents into a knowledge graph stored in ArangoDB.
 
 ## Search methods