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Transform unstructured data (PDFs, DOCs, TXT, YouTube videos, web pages, etc.) into a structured Knowledge Graph stored in Neo4j using the power of Large Language Models (LLMs) and the LangChain framework.

This application allows you to upload files from various sources (local machine, GCS, S3 bucket, or web sources), choose your preferred LLM model, and generate a Knowledge Graph.

• Seamlessly transform unstructured data into structured Knowledge Graphs using advanced LLMs.
• Extract nodes, relationships, and their properties to create structured graphs.

• Use a custom schema or existing schemas configured in the settings to generate graphs.

• View graphs for specific or multiple data sources simultaneously inNeo4j Bloom.

• Interact with your data in the Neo4j database through conversational queries.
• Retrieve metadata about the source of responses to your queries.
• For a dedicated chat interface, use the standalone chat application with/chat-only route.

1. OpenAI
2. Gemini
3. Diffbot
4. Azure OpenAI(dev deployed version)
5. Anthropic(dev deployed version)
6. Fireworks(dev deployed version)
7. Groq(dev deployed version)
8. Amazon Bedrock(dev deployed version)
9. Ollama(dev deployed version)
10. Deepseek(dev deployed version)
11. Other OpenAI Compatible baseurl models(dev deployed version)

• Neo4j Database5.23 or later with APOC installed.
  • Neo4j Aura databases (including the free tier) are supported.
  • If usingNeo4j Desktop, you will need to deploy the backend and frontend separately (docker-compose is not supported).

Run the application using the defaultdocker-compose configuration.

1. Supported LLM Models:

   • By default, only OpenAI and Diffbot are enabled. Gemini requires additional GCP configurations.
   • Use theVITE_LLM_MODELS_PROD variable to configure the models you need. Example:

     VITE_LLM_MODELS_PROD="openai_gpt_4o,openai_gpt_4o_mini,diffbot,gemini_1.5_flash"

2. Input Sources:

   • By default, the following sources are enabled:local,YouTube,Wikipedia,AWS S3, andweb.
   • To add Google Cloud Storage (GCS) integration, includegcs and your Google client ID:

     VITE_REACT_APP_SOURCES="local,youtube,wiki,s3,gcs,web"VITE_GOOGLE_CLIENT_ID="your-google-client-id"

Configure chat modes using theVITE_CHAT_MODES variable:

• By default, all modes are enabled:vector,graph_vector,graph,fulltext,graph_vector_fulltext,entity_vector, andglobal_vector.
• To specify specific modes, update the variable. For example:

  VITE_CHAT_MODES="vector,graph"

For development, you can run the backend and frontend independently.

1. Create the.env file in thefrontend folder by copyingfrontend/example.env.
2. Update environment variables as needed.
3. Run:

   cd frontendyarnyarn run dev

1. Create the.env file in thebackend folder by copyingbackend/example.env.
2. Preconfigure user credentials in the.env file to bypass the login dialog:

   NEO4J_URI=<your-neo4j-uri>NEO4J_USERNAME=<your-username>NEO4J_PASSWORD=<your-password>NEO4J_DATABASE=<your-database-name>

3. Run:

   cd backendpython -m venv envNamesource envName/bin/activatepip install -r requirements.txtuvicorn score:app --reload

Deploy the application onGoogle Cloud Platform using the following commands:

gcloud run deploy dev-frontend \  --source. \  --region us-central1 \  --allow-unauthenticated

gcloud run deploy dev-backend \  --set-env-vars"OPENAI_API_KEY=<your-openai-api-key>" \  --set-env-vars"DIFFBOT_API_KEY=<your-diffbot-api-key>" \  --set-env-vars"NEO4J_URI=<your-neo4j-uri>" \  --set-env-vars"NEO4J_USERNAME=<your-username>" \  --set-env-vars"NEO4J_PASSWORD=<your-password>" \  --source. \  --region us-central1 \  --allow-unauthenticated

1. Pull the docker imgage of ollama

docker pull ollama/ollama

2. Run the ollama docker image

docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

3. Execute any llm model ex llama3

dockerexec -it ollama ollama run llama3

4. Configure env variable in docker compose.

LLM_MODEL_CONFIG_ollama_<model_name>#exampleLLM_MODEL_CONFIG_ollama_llama3=${LLM_MODEL_CONFIG_ollama_llama3-llama3,http://host.docker.internal:11434}

5. Configure the backend API url

VITE_BACKEND_API_URL=${VITE_BACKEND_API_URL-backendurl}

6. Open the application in browser and select the ollama model for the extraction.
7. Enjoy Graph Building.

1. Connect to Neo4j Aura Instance which can be both AURA DS or AURA DB by passing URI and password through Backend env, fill using login dialog or drag and drop the Neo4j credentials file.
2. To differntiate we have added different icons. For AURA DB we have a database icon and for AURA DS we have scientific molecule icon right under Neo4j Connection details label.
3. Choose your source from a list of Unstructured sources to create graph.
4. Change the LLM (if required) from drop down, which will be used to generate graph.
5. Optionally, define schema(nodes and relationship labels) in entity graph extraction settings.
6. Either select multiple files to 'Generate Graph' or all the files in 'New' status will be processed for graph creation.
7. Have a look at the graph for individual files using 'View' in grid or select one or more files and 'Preview Graph'
8. Ask questions related to the processed/completed sources to chat-bot, Also get detailed information about your answers generated by LLM.

LLM Knowledge Graph Builder Application

Neo4j Workspace

Demo of application

For any inquiries or support, feel free to raiseGithub Issue