Added GraphRAG uses cases, reorganize content

site/content/3.12/data-science/graphrag/_index.md

@@ -1,7 +1,7 @@
 ---
 title: GraphRAG
 menuTitle: GraphRAG
-weight: 10
+weight: 5
 description: >-
   ArangoDB's GraphRAG solution combines graph-based retrieval-augmented generation
   with Large Language Models (LLMs) for turbocharged GenAI solutions
@@ -16,148 +16,47 @@ exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}
 
-## Introduction
+## Transform unstructured documents into intelligent knowledge graphs
 
-Large language models (LLMs) and knowledge graphs are two prominent and
-contrasting concepts, each possessing unique characteristics and functionalities
-that significantly impact the methods we employ to extract valuable insights from
-constantly expanding and complex datasets.
+ArangoDB's GraphRAG solution enables organizations to extract meaningful insights 
+from their document collections by creating knowledge graphs that capture not just 
+individual facts, but the intricate relationships between concepts across documents. 
+This approach goes beyond traditional RAG systems by understanding document 
+interconnections and providing both granular detail-level responses and high-level 
+conceptual understanding.
 
-LLMs, such as those powering OpenAI's ChatGPT, represent a class of powerful language
-transformers. These models leverage advanced neural networks to exhibit a
-remarkable proficiency in understanding, generating, and participating in
-contextually-aware conversations.
+- **Intelligent document understanding**: Automatically extracts and connects knowledge across multiple document sources
+- **Contextual intelligence**: Maintains relationships between concepts, enabling more accurate and comprehensive responses  
+- **Multi-level insights**: Provides both detailed technical answers and strategic high-level understanding
+- **Seamless knowledge access**: Natural language interface for querying complex document relationships
 
-On the other hand, knowledge graphs contain carefully structured data and are
-designed to capture intricate relationships among discrete and seemingly
-unrelated information.
+## Key benefits for enterprise applications
 
-ArangoDB's unique capabilities and flexible integration of knowledge graphs and
-LLMs provide a powerful and efficient solution for anyone seeking to extract
-valuable insights from diverse datasets.
+- **Cross-document relationship intelligence**:
+Unlike traditional RAG systems that treat documents in isolation, ArangoDB's GraphRAG 
+pipeline detects and leverages references between documents and chunks. This enables 
+more accurate responses by understanding how concepts relate across your entire knowledge base.
 
-The GraphRAG component of the GenAI Suite brings all the capabilities
-together with an easy-to-use interface, so you can make the knowledge accessible
-to your organization.
+- **Multi-level understanding architecture**:
+The system provides both detailed technical responses and high-level strategic insights 
+from the same knowledge base, adapting response depth based on query complexity and user intent.
 
-GraphRAG is particularly valuable for use cases like the following:
-- Applications requiring in-depth knowledge retrieval
-- Contextual question answering
-- Reasoning over interconnected information
+- **Reference-aware knowledge graph**:
+GraphRAG automatically detects and maps relationships between document chunks while 
+maintaining context of how information connects across different sources.
 
-## How GraphRAG works
+- **Dynamic knowledge evolution**:
+The system learns and improves understanding as more documents are added, with 
+relationships and connections becoming more sophisticated over time.
 
-ArangoDB's GraphRAG solution democratizes the creation and usage of knowledge
-graphs with a unique combination of vector search, graphs, and LLMs (privately or publicly hosted)
-in a single product.
 
-The overall workflow involves the following steps:
-1. **Chunking**:
-   - Breaking down raw documents into text chunks
-2. **Entity and relation extraction for Knowledge Graph construction**:
-   - LLM-assisted description of entities and relations
-   - Entities get inserted as nodes with embeddings
-   - Relations get inserted as edges, these include: entity-entity, entity-chunk, chunk-document
-3. **Topology-based clustering into mini-topics (called communities)**:
-   - Each entity points to its community
-   - Each community points to its higher-level community, if available
-     (mini-topics point to major topics)
-4. **LLM-assisted community summarization**:
-   - Community summarization is based on all information available about each topic
+## What's next
 
-### Turn text files into a Knowledge Graph
+- **[GraphRAG Enterprise Use Cases](use-cases.md)**: Understand the business value through real-world scenarios.
+- **[GraphRAG Technical Overview](technical-overview.md)**: Dive into the architecture, services, and implementation details.
+- **[GraphRAG Web Interface](web-interface.md)**: Try GraphRAG using the interactive web interface.
+- **[GraphRAG Tutorial using integrated Notebook servers](tutorial-notebook.md)**: Follow hands-on examples and implementation guidance via Jupyter Notebooks.
 
-The Importer service is the entry point of the GraphRAG pipeline. It takes a
-raw text file as input, processes it using an LLM to extract entities and
-relationships, and generates a Knowledge Graph. The Knowledge Graph is then
-stored in an ArangoDB database for further use. The Knowledge Graph represents
-information in a structured graph format, allowing efficient querying and retrieval.
-
-1. Pre-process the raw text file to identify entities and their relationships.
-2. Use LLMs to infer connections and context, enriching the Knowledge Graph.
-3. Store the generated Knowledge Graph in the database for retrieval and reasoning.
-
-For detailed information about the service, see the
-[Importer](services/importer.md) service documentation.
-
-### Extract information from the Knowledge Graph
-
-The Retriever service enables intelligent search and retrieval of information
-from your previously created Knowledge Graph.
-You can extract information from Knowledge Graphs using two distinct methods:
-- Global retrieval
-- Local retrieval
-
-For detailed information about the service, see the
-[Retriever](services/retriever.md) service documentation.
-
-#### Global retrieval
-
-Global retrieval focuses on:
-- Extracting information from the entire Knowledge Graph, regardless of specific
-  contexts or constraints.
-- Provides a comprehensive overview and answers queries that span across multiple
-  entities and relationships in the graph.
-
-**Use cases:**
-- Answering broad questions that require a holistic understanding of the Knowledge Graph.
-- Aggregating information from diverse parts of the Knowledge Graph for high-level insights.
-
-**Example query:**
-
-Global retrieval can answer questions like _**What are the main themes or topics covered in the document**_?
-
-During import, the entire Knowledge Graph is analyzed to identify and summarize
-the dominant entities, their relationships, and associated themes. Global
-retrieval uses these community summaries to answer questions from different
-perspectives, then the information gets aggregated into the final response.
-
-#### Local retrieval
-
-Local retrieval is a more focused approach for:
-- Queries that are constrained to specific subgraphs or contextual clusters
-  within the Knowledge Graph.
-- Targeted and precise information extraction, often using localized sections
-  of the Knowledge Graph.
-
-**Use cases:**
-- Answering detailed questions about a specific entity or a related group of entities.
-- Retrieving information relevant to a particular topic or section in the Knowledge Graph.
-
-**Example query:**
-
-Local retrieval can answer questions like _**What is the relationship between entity X and entity Y**_?
-
-Local queries use hybrid search (semantic and lexical) over the Entities
-collection, and then it expands that subgraph over related entities, relations
-(and its LLM-generated verbal descriptions), text chunks, and communities.
-
-### Private LLMs
-
-If you're working in an air-gapped environment or need to keep your data
-private, you can use the private LLM mode with 
-[Triton Inference Server](services/triton-inference-server.md).
-
-This option allows you to run the service completely within your own
-infrastructure. The Triton Inference Server is a crucial component when
-running in private LLM mode. It serves as the backbone for running your
-language (LLM) and embedding models on your own machines, ensuring your
-data never leaves your infrastructure. The server handles all the complex
-model operations, from processing text to generating embeddings, and provides
-both HTTP and gRPC interfaces for communication.
-
-### Public LLMs
-
-Alternatively, if you prefer a simpler setup and don't have specific privacy
-requirements, you can use the public LLM mode. This option connects to cloud-based
-services like OpenAI's models via the OpenAI API or a large array of models
-(Gemini, Anthropic, publicly hosted open-source models, etc.) via the OpenRouter option.
-
-## Limitations
-
-The pre-release version of ArangoDB GraphRAG has the following limitations:
-
-- You can only import a single file.
-- The knowledge graph generated from the file is imported into a named graph
-  with a fixed name of `KnowledgeGraph` and set of collections which also have
-  fixed names.
+For deeper implementation details, explore the individual services:
+- **[Importer Service](services/importer.md)**: Transform documents into knowledge graphs.
+- **[Retriever Service](services/retriever.md)**: Query and extract insights from your knowledge graphs.

site/content/3.12/data-science/graphrag/technical-overview.md

@@ -0,0 +1,161 @@
+---
+title: GraphRAG Technical Overview
+menuTitle: Technical Overview
+weight: 15
+description: >-
+  Technical overview of ArangoDB's GraphRAG solution, including
+  architecture, services, and deployment options
+---
+{{< tag "ArangoDB Platform" >}}
+
+{{< tip >}}
+The ArangoDB Platform & GenAI Suite is available as a pre-release. To get
+exclusive early access, [get in touch](https://arangodb.com/contact/) with
+the ArangoDB team.
+{{< /tip >}}
+
+## Introduction
+
+Large language models (LLMs) and knowledge graphs are two prominent and
+contrasting concepts, each possessing unique characteristics and functionalities
+that significantly impact the methods we employ to extract valuable insights from
+constantly expanding and complex datasets.
+
+LLMs, such as those powering OpenAI's ChatGPT, represent a class of powerful language
+transformers. These models leverage advanced neural networks to exhibit a
+remarkable proficiency in understanding, generating, and participating in
+contextually-aware conversations.
+
+On the other hand, knowledge graphs contain carefully structured data and are
+designed to capture intricate relationships among discrete and seemingly
+unrelated information.
+
+ArangoDB's unique capabilities and flexible integration of knowledge graphs and
+LLMs provide a powerful and efficient solution for anyone seeking to extract
+valuable insights from diverse datasets.
+
+The GraphRAG component of the GenAI Suite brings all the capabilities
+together with an easy-to-use interface, so you can make the knowledge accessible
+to your organization.
+
+GraphRAG is particularly valuable for use cases like the following:
+- Applications requiring in-depth knowledge retrieval
+- Contextual question answering
+- Reasoning over interconnected information
+
+## How GraphRAG works
+
+ArangoDB's GraphRAG solution democratizes the creation and usage of knowledge
+graphs with a unique combination of vector search, graphs, and LLMs (privately or publicly hosted)
+in a single product.
+
+The overall workflow involves the following steps:
+1. **Chunking**:
+   - Breaking down raw documents into text chunks
+2. **Entity and relation extraction for Knowledge Graph construction**:
+   - LLM-assisted description of entities and relations
+   - Entities get inserted as nodes with embeddings
+   - Relations get inserted as edges, these include: entity-entity, entity-chunk, chunk-document
+3. **Topology-based clustering into mini-topics (called communities)**:
+   - Each entity points to its community
+   - Each community points to its higher-level community, if available
+     (mini-topics point to major topics)
+4. **LLM-assisted community summarization**:
+   - Community summarization is based on all information available about each topic
+
+### Turn text files into a Knowledge Graph
+
+The Importer service is the entry point of the GraphRAG pipeline. It takes a
+raw text file as input, processes it using an LLM to extract entities and
+relationships, and generates a Knowledge Graph. The Knowledge Graph is then
+stored in an ArangoDB database for further use. The Knowledge Graph represents
+information in a structured graph format, allowing efficient querying and retrieval.
+
+1. Pre-process the raw text file to identify entities and their relationships.
+2. Use LLMs to infer connections and context, enriching the Knowledge Graph.
+3. Store the generated Knowledge Graph in the database for retrieval and reasoning.
+
+For detailed information about the service, see the
+[Importer](services/importer.md) service documentation.
+
+### Extract information from the Knowledge Graph
+
+The Retriever service enables intelligent search and retrieval of information
+from your previously created Knowledge Graph.
+You can extract information from Knowledge Graphs using two distinct methods:
+- Global retrieval
+- Local retrieval
+
+For detailed information about the service, see the
+[Retriever](services/retriever.md) service documentation.
+
+#### Global retrieval
+
+Global retrieval focuses on:
+- Extracting information from the entire Knowledge Graph, regardless of specific
+  contexts or constraints.
+- Provides a comprehensive overview and answers queries that span across multiple
+  entities and relationships in the graph.
+
+**Use cases:**
+- Answering broad questions that require a holistic understanding of the Knowledge Graph.
+- Aggregating information from diverse parts of the Knowledge Graph for high-level insights.
+
+**Example query:**
+
+Global retrieval can answer questions like _**What are the main themes or topics covered in the document**_?
+
+During import, the entire Knowledge Graph is analyzed to identify and summarize
+the dominant entities, their relationships, and associated themes. Global
+retrieval uses these community summaries to answer questions from different
+perspectives, then the information gets aggregated into the final response.
+
+#### Local retrieval
+
+Local retrieval is a more focused approach for:
+- Queries that are constrained to specific subgraphs or contextual clusters
+  within the Knowledge Graph.
+- Targeted and precise information extraction, often using localized sections
+  of the Knowledge Graph.
+
+**Use cases:**
+- Answering detailed questions about a specific entity or a related group of entities.
+- Retrieving information relevant to a particular topic or section in the Knowledge Graph.
+
+**Example query:**
+
+Local retrieval can answer questions like _**What is the relationship between entity X and entity Y**_?
+
+Local queries use hybrid search (semantic and lexical) over the Entities
+collection, and then it expands that subgraph over related entities, relations
+(and its LLM-generated verbal descriptions), text chunks, and communities.
+
+### Private LLMs
+
+If you're working in an air-gapped environment or need to keep your data
+private, you can use the private LLM mode with 
+[Triton Inference Server](services/triton-inference-server.md).
+
+This option allows you to run the service completely within your own
+infrastructure. The Triton Inference Server is a crucial component when
+running in private LLM mode. It serves as the backbone for running your
+language (LLM) and embedding models on your own machines, ensuring your
+data never leaves your infrastructure. The server handles all the complex
+model operations, from processing text to generating embeddings, and provides
+both HTTP and gRPC interfaces for communication.
+
+### Public LLMs
+
+Alternatively, if you prefer a simpler setup and don't have specific privacy
+requirements, you can use the public LLM mode. This option connects to cloud-based
+services like OpenAI's models via the OpenAI API or a large array of models
+(Gemini, Anthropic, publicly hosted open-source models, etc.) via the OpenRouter option.
+
+## Limitations
+
+The pre-release version of ArangoDB GraphRAG has the following limitations:
+
+- You can only import a single file.
+- The knowledge graph generated from the file is imported into a named graph
+  with a fixed name of `KnowledgeGraph` and set of collections which also have
+  fixed names.

site/content/3.12/data-science/graphrag/tutorial-notebook.md

@@ -3,7 +3,7 @@ title: GraphRAG Notebook Tutorial
 menuTitle: Notebook Tutorial
 description: >-
   Building a GraphRAG pipeline using ArangoDB's integrated notebook servers
-weight: 10
+weight: 25
 ---
 {{< tag "ArangoDB Platform" >}}