Updates for features (graph-viz) and deployment (fixes code listing)

modules/genai-ecosystem/pages/llm-graph-builder-deployment.adoc

@@ -22,27 +22,35 @@ If want to use https://neo4j.com/product/developer-tools/#desktop[Neo4j Desktop^
 By default only OpenAI and Diffbot are enabled since Gemini requires extra GCP configurations.
 
 In your root folder, create a .env file with your OPENAI and DIFFBOT keys (if you want to use both):
-```env
+
+[source,env]
+----
 OPENAI_API_KEY="your-openai-key"
 DIFFBOT_API_KEY="your-diffbot-key"
-```
+----
 
 if you only want OpenAI:
-```env
+
+[source,env]
+----
 LLM_MODELS="gpt-3.5,gpt-4o"
 OPENAI_API_KEY="your-openai-key"
-```
+----
 
 if you only want Diffbot:
-```env
+
+[source,env]
+----
 LLM_MODELS="diffbot"
 DIFFBOT_API_KEY="your-diffbot-key"
-```
+----
 
 You can then run Docker Compose to build and start all components:
-```bash
+
+[source,bash]
+----
 docker-compose up --build
-```
+----
 
 === Configuring LLM Models
 
@@ -61,7 +69,7 @@ To achieve that you need to set a number of environment variables:
 In your `.env` file, add the following lines. You can of course also add other model configurations from these providers or any OpenAI API compatible provider.
 
 [source,env]
-====
+----
 LLM_MODEL_CONFIG_azure_ai_gpt_35="gpt-35,https://<deployment>.openai.azure.com/,<api-key>,<version>"
 LLM_MODEL_CONFIG_anthropic_claude_35_sonnet="claude-3-5-sonnet-20240620,<api-key>"
 LLM_MODEL_CONFIG_fireworks_llama_v3_70b="accounts/fireworks/models/llama-v3-70b-instruct,<api-key>"
@@ -71,12 +79,12 @@ LLM_MODEL_CONFIG_fireworks_qwen_72b="accounts/fireworks/models/qwen2-72b-instruc
 
 # Optional Frontend config
 LLM_MODELS="diffbot,gpt-3.5,gpt-4o,azure_ai_gpt_35,azure_ai_gpt_4o,groq_llama3_70b,anthropic_claude_35_sonnet,fireworks_llama_v3_70b,bedrock_claude_35_sonnet,ollama_llama3,fireworks_qwen_72b"
-====
+----
 
 In your `docker-compose.yml` you need to pass the variables through:
 
 [source,yaml]
-====
+----
 - LLM_MODEL_CONFIG_anthropic_claude_35_sonnet=${LLM_MODEL_CONFIG_anthropic_claude_35_sonnet-}
 - LLM_MODEL_CONFIG_fireworks_llama_v3_70b=${LLM_MODEL_CONFIG_fireworks_llama_v3_70b-}
 - LLM_MODEL_CONFIG_azure_ai_gpt_4o=${LLM_MODEL_CONFIG_azure_ai_gpt_4o-}
@@ -85,21 +93,25 @@ In your `docker-compose.yml` you need to pass the variables through:
 - LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet=${LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet-}
 - LLM_MODEL_CONFIG_fireworks_qwen_72b=${LLM_MODEL_CONFIG_fireworks_qwen_72b-}
 - LLM_MODEL_CONFIG_ollama_llama3=${LLM_MODEL_CONFIG_ollama_llama3-}
-====
+----
 
 === Additional configs
 
 By default, the input sources will be: Local files, Youtube, Wikipedia and AWS S3. 
 This is the default config applied if you do not overwrite it in your .env file:
-```env
+
+[source,env]
+----
 REACT_APP_SOURCES="local,youtube,wiki,s3"
-```
+----
 
 If however you want the Google GCS integration, add `gcs` and your Google client ID:
-```env
+
+[source,env]
+----
 REACT_APP_SOURCES="local,youtube,wiki,s3,gcs"
 GOOGLE_CLIENT_ID="xxxx"
-```
+----
 
 The `REACT_APP_SOURCES` should be a comma-separated list of the sources you want to enable.
 You can of course combine all (local, youtube, wikipedia, s3 and gcs) or remove any you don't want or need.
@@ -113,23 +125,27 @@ Alternatively, you can run the backend and frontend separately:
 1. Create the frontend/.env file by copy/pasting the frontend/example.env.
 2. Change values as needed
 3. Run:
-```bash
+
+[source,bash]
+----
 cd frontend
 yarn
 yarn run dev
-```
+----
 
 - For the backend:
 1. Create the backend/.env file by copy/pasting the backend/example.env.
 2. Change values as needed
 3.  Run:
-```bash
+
+[source,bash]
+----
 cd backend
 python -m venv envName
 source envName/bin/activate 
 pip install -r requirements.txt
 uvicorn score:app --reload
-```
+----
 
 == ENV
 

modules/genai-ecosystem/pages/llm-graph-builder-features.adoc

@@ -71,11 +71,11 @@ image::llm-graph-builder-taxonomy.png[width=600, align=center]
 
 If you want to use a pre-defined or your own graph schema, you can do so in the Graph Enhancements popup. This is also shown the first time you construct a graph and the state of the model configuration is listed below the connection information.
 
- You can either:
- * select a pre-defined schema from the dropdown on top,
- * use your own by entering the node labels and relationships, 
- * fetch the existing schema from an existing Neo4j database (`Use Existing Schema`), 
- * or copy/paste a text or schema description (also works with RDF ontologies or Cypher/GQL schema) and ask the LLM to analyze it and come up with a suggested schema (`Get Schema From Text`).
+You can either:
+* select a pre-defined schema from the dropdown on top,
+* use your own by entering the node labels and relationships, 
+* fetch the existing schema from an existing Neo4j database (`Use Existing Schema`), 
+* or copy/paste a text or schema description (also works with RDF ontologies or Cypher/GQL schema) and ask the LLM to analyze it and come up with a suggested schema (`Get Schema From Text`).
 
 === Delete Disconnected Nodes
 
@@ -94,6 +94,31 @@ Here we use a mixture of entity embedding, edit distance and substring containme
 You can select which sets of entities should be merged and exclude certain entities from the merge.
 ////
 
+== Visualization
+
+You can visualize the _lexical_, the _entity_ graph or the full _knowledge_ graph of your extracted documents.
+
+=== Graph Visualization
+
+You have 2 options - either per document with the magifying glass icon at the end of the table or for all selected documents with the "Preview Graph" button.
+
+The graph visualization will show for the relevant files in a pop-up and you can filter for the type of graph you wanto to see:
+
+- Lexical Graph - the document and chunk nodes and their relationships
+- Entity Graph - the entity nodes and their relationships
+- Full Graph - all nodes and relationships
+
+=== Explore in Neo4j Bloom
+
+With the button "Explore in Neo4j Bloom" you can open the constructed knowledge graph in Neo4j Workspace for further visual exploration, querying and analysis.
+
+In Bloom/Explore you can run low code pattern based queries (or use the co-pilot) to fetch data from the graph and succesfully expand. If you are running against a GDS enabled instance, you can also run graph algorithms and visualize the results.
+You can also interactively edit and add to the graph.
+
+In Neo4j Data Importer you can additionally import structured data from CSV files and connect it to your extracted knowledge graph.
+
+In Neo4j Query you can write Cypher queries (or use the co-pilot) to pull tabular and graph data from your database.
+
 == Chatbot
 
 === How it works
@@ -105,9 +130,10 @@ We also summarize the chat history and use it as an element to enrich the contex
 === Features
 
 - *Select RAG Mode* you can select vector-only or GraphRAG (vector+graph) modes
+- *Chat with selected documents:* Will use the selected documents only for RAG, uses pre-filtering to achieve that
+- *Details:* Will open a Retrieval information pop-up showing details on how the RAG agent collected and used sources (documents), chunks, and entities. Also provides information on the model used and the token consumption.
 - *Clear chat:* Will delete the current session's chat history.
 - *Expand view:* Will open the chatbot interface in a fullscreen mode.
-- *Details:* Will open a Retrieval information pop-up showing details on how the RAG agent collected and used sources (documents), chunks, and entities. Also provides information on the model used and the token consumption.
 - *Copy:* Will copy the content of the response to the clipboard.
 - *Text-To-Speech:* Will read out loud the content of the response.