Knowledge Graph Builder

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.

Getting Started

Prerequisites

• Python 3.12 or higher (for local/separate backend deployment)
• Neo4j Database 5.23 or later with APOC installed.
  • Neo4j Aura databases (including the free tier) are supported.
  • If using Neo4j Desktop, you will need to deploy the backend and frontend separately (docker-compose is not supported).

Backend Setup

1. Create the .env file in the backend folder by copying backend/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 backend
   python3.12 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt -c constraints.txt
   uvicorn score:app --reload
   

Key Features

Knowledge Graph Creation

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

Schema Support

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

Graph Visualization

• View graphs for specific or multiple data sources simultaneously in Neo4j Bloom.

Chat with Data

• 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.

LLMs Supported

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)

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Getting Started

Prerequisites

• Neo4j Database 5.23 or later with APOC installed.
  • Neo4j Aura databases (including the free tier) are supported.
  • If using Neo4j Desktop, you will need to deploy the backend and frontend separately (docker-compose is not supported).

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Deployment Options

Local Deployment

Using Docker-Compose

Run the application using the default docker-compose configuration.

1. Supported LLM Models:

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

     VITE_LLM_MODELS_PROD="openai_gpt_5_mini,diffbot,gemini_2.5_flash"

2. Input Sources:

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

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

Chat Modes

Configure chat modes using the VITE_CHAT_MODES variable:

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

  VITE_CHAT_MODES="vector,graph"

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Running Backend and Frontend Separately

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

Frontend Setup

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

   cd frontend
   yarn
   yarn run dev

Backend Setup

1. Create the .env file in the backend folder by copying backend/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 backend
   python -m venv envName
   source envName/bin/activate
   pip install -r requirements.txt
   uvicorn score:app --reload

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Cloud Deployment

Deploy the application on Google Cloud Platform using the following commands:

Frontend Deployment

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

Backend Deployment

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

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For local llms (Ollama)

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

docker exec -it ollama ollama run llama3

4. Configure env variable in docker compose.

LLM_MODEL_CONFIG_ollama_<model_name>
#example
LLM_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.

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Usage

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.

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ENV

Env Variable Name	Mandatory/Optional	Default Value	Description
BACKEND ENV
OPENAI_API_KEY	Mandatory		An OpenAI Key is required to use OpenAI LLM model to authenticate and track requests
DIFFBOT_API_KEY	Mandatory		API key is required to use Diffbot's NLP service to extract entities and relationships from unstructured data
BUCKET	Mandatory		Bucket name to store uploaded files on GCS
NEO4J_USER_AGENT	Optional	llm-graph-builder	Name of the user agent to track Neo4j database activity
ENABLE_USER_AGENT	Optional	true	Boolean value to enable/disable Neo4j user agent
DUPLICATE_TEXT_DISTANCE	Mandatory	5	This value is used to find distance for all node pairs in the graph and is calculated based on node properties
DUPLICATE_SCORE_VALUE	Mandatory	0.97	Node score value to match duplicate nodes
EFFECTIVE_SEARCH_RATIO	Mandatory	1	Ratio used for effective search calculations
GRAPH_CLEANUP_MODEL	Optional	openai_gpt_4o_mini	Model name to clean up graph in post processing
MAX_TOKEN_CHUNK_SIZE	Optional	10000	Maximum token size to process file content
YOUTUBE_TRANSCRIPT_PROXY	Optional		Proxy key to process YouTube videos for getting transcripts
EMBEDDING_MODEL	Optional		Model for generating text embeddings (default: all-MiniLM-L6-v2, openai, vertexai, titan)
IS_EMBEDDING	Optional	true	Flag to enable text embedding
KNN_MIN_SCORE	Optional	0.94	Minimum score for KNN algorithm
GEMINI_ENABLED	Optional	False	Flag to enable Gemini
GCP_LOG_METRICS_ENABLED	Optional	False	Flag to enable Google Cloud logs
NUMBER_OF_CHUNKS_TO_COMBINE	Optional	5	Number of chunks to combine when processing embeddings
UPDATE_GRAPH_CHUNKS_PROCESSED	Optional	20	Number of chunks processed before updating progress
NEO4J_URI	Optional	neo4j://database:7687	URI for Neo4j database
NEO4J_USERNAME	Optional	neo4j	Username for Neo4j database
NEO4J_PASSWORD	Optional	password	Password for Neo4j database
LANGCHAIN_API_KEY	Optional		API key for Langchain
LANGCHAIN_PROJECT	Optional		Project for Langchain
LANGCHAIN_TRACING_V2	Optional	true	Flag to enable Langchain tracing
GCS_FILE_CACHE	Optional	False	If set to True, will save files to process into GCS. If False, will save files locally
LANGCHAIN_ENDPOINT	Optional	Langchain Endpoint	Endpoint for Langchain API
ENTITY_EMBEDDING	Optional	False	If set to True, it will add embeddings for each entity in the database
LLM_MODEL_CONFIG_ollama_<model_name>	Optional		Set ollama config as model_name,model_local_url for local deployments
RAGAS_EMBEDDING_MODEL	Optional		Embedding model used by RAGAS evaluation framework
FRONTEND ENV
VITE_BLOOM_URL	Mandatory	Bloom URL	URL for Bloom visualization
VITE_REACT_APP_SOURCES	Mandatory	local,youtube,wiki,s3	List of input sources that will be available
VITE_CHAT_MODES	Mandatory	vector,graph+vector,graph,hybrid	Chat modes available for Q&A
VITE_ENV	Mandatory	DEV or PROD	Environment variable for the app
VITE_LLM_MODELS	Optional	openai_gpt_5_mini,gemini_2.5_flash,anthropic_claude_4.5_haiku
Supported models for the application
VITE_BACKEND_API_URL	Optional	localhost	URL for backend API
VITE_TIME_PER_PAGE	Optional	50	Time per page for processing
VITE_CHUNK_SIZE	Optional	5242880	Size of each chunk of file for upload
VITE_GOOGLE_CLIENT_ID	Optional		Client ID for Google authentication
VITE_LLM_MODELS_PROD	Optional	openai_gpt_5_mini,gemini_2.5_flash,anthropic_claude_4.5_haiku	To distinguish models based on environment (PROD or DEV)
VITE_AUTH0_CLIENT_ID	Mandatory if you are enabling Authentication otherwise it is optional		Okta OAuth Client ID for authentication
VITE_AUTH0_DOMAIN	Mandatory if you are enabling Authentication otherwise it is optional		Okta OAuth Client Domain
VITE_SKIP_AUTH	Optional	true	Flag to skip authentication
VITE_CHUNK_OVERLAP	Optional	20	Variable to configure chunk overlap
VITE_TOKENS_PER_CHUNK	Optional	100	Variable to configure tokens count per chunk. This gives flexibility for users who may require different chunk sizes for various tokenization tasks
VITE_CHUNK_TO_COMBINE	Optional	1	Variable to configure number of chunks to combine for parallel processing

Refer to this link for additional Environment Variables.

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Links

LLM Knowledge Graph Builder Application

Neo4j Workspace

Reference

Demo of application

Contact

For any inquiries or support, feel free to raise GitHub Issues

Happy Graph Building!