Knowledge Graph Builder App

Creating knowledge graphs from unstructured data

LLM Graph Builder

Overview

This application is designed to turn Unstructured data (pdfs,docs,txt,youtube video,web pages,etc.) into a knowledge graph stored in Neo4j. It utilizes the power of Large language models (OpenAI,Gemini,etc.) to extract nodes, relationships and their properties from the text and create a structured knowledge graph using Langchain framework.

Upload your files from local machine, GCS or S3 bucket or from web sources, choose your LLM model and generate knowledge graph.

Key Features

• Knowledge Graph Creation: Transform unstructured data into structured knowledge graphs using LLMs.
• Providing Schema: Provide your own custom schema or use existing schema in settings to generate graph.
• View Graph: View graph for a particular source or multiple sources at a time in Bloom.
• Chat with Data: Interact with your data in a Neo4j database through conversational queries, also retrieve metadata about the source of response to your queries.For a dedicated chat interface, access the standalone chat application at: Chat-Only. This link provides a focused chat experience for querying your data.

Getting started

:warning: You will need to have a Neo4j Database V5.15 or later with APOC installed to use this Knowledge Graph Builder. You can use any Neo4j Aura database (including the free database) If you are using Neo4j Desktop, you will not be able to use the docker-compose but will have to follow the separate deployment of backend and frontend section. :warning:

Deployment

Local deployment

Running through docker-compose

By default only OpenAI and Diffbot are enabled since Gemini requires extra GCP configurations. Accoroding to enviornment we are configuring the models which is indicated by VITE_LLM_MODELS_PROD variable we can configure model based on our need. EX:

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

According to the environment, we are configuring the models which indicated by VITE_LLM_MODELS_PROD variable we can configure models based on our needs. EX:

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

if you only want OpenAI:

VITE_LLM_MODELS_PROD="diffbot,openai-gpt-3.5,openai-gpt-4o"
VITE_LLM_MODELS_PROD="diffbot,openai-gpt-3.5,openai-gpt-4o"
OPENAI_API_KEY="your-openai-key"

if you only want Diffbot:

VITE_LLM_MODELS_PROD="diffbot"
VITE_LLM_MODELS_PROD="diffbot"
DIFFBOT_API_KEY="your-diffbot-key"

You can then run Docker Compose to build and start all components:

docker-compose up --build

Additional configs

By default, the input sources will be: Local files, Youtube, Wikipedia ,AWS S3 and Webpages. As this default config is applied:

VITE_REACT_APP_SOURCES="local,youtube,wiki,s3,web"

If however you want the Google GCS integration, add gcs and your Google client ID:

VITE_REACT_APP_SOURCES="local,youtube,wiki,s3,gcs,web"
VITE_GOOGLE_CLIENT_ID="xxxx"

You can of course combine all (local, youtube, wikipedia, s3 and gcs) or remove any you don't want/need.

Chat Modes

By default,all of the chat modes will be available: vector, graph_vector, graph, fulltext, graph_vector_fulltext , entity_vector and global_vector. By default,all of the chat modes will be available: vector, graph_vector, graph, fulltext, graph_vector_fulltext , entity_vector and global_vector. If none of the mode is mentioned in the chat modes variable all modes will be available:

VITE_CHAT_MODES=""

If however you want to specify the only vector mode or only graph mode you can do that by specifying the mode in the env:

VITE_CHAT_MODES="vector,graph"
VITE_CHAT_MODES="vector,graph"

Running Backend and Frontend separately (dev environment)

Alternatively, you can run the backend and frontend separately:

• For the frontend:

1. Create the frontend/.env file by copy/pasting the frontend/example.env.
2. Change values as needed

3. cd frontend
   yarn
   yarn run dev
   

• For the backend:

1. Create the backend/.env file by copy/pasting the backend/example.env. To streamline the initial setup and testing of the application, you can preconfigure user credentials directly within the .env file. This bypasses the login dialog and allows you to immediately connect with a predefined user.
   • NEO4J_URI:
   • NEO4J_USERNAME:
   • NEO4J_PASSWORD:
   • NEO4J_DATABASE:
2. Change values as needed

3. cd backend
   python -m venv envName
   source envName/bin/activate 
   pip install -r requirements.txt
   uvicorn score:app --reload
   

Deploy in Cloud

To deploy the app and packages on Google Cloud Platform, run the following command on google cloud run:

# Frontend deploy 
gcloud run deploy dev-frontend --set-env-vars "VITE_BACKEND_API_URL=" --set-env-vars "VITE_FRONTEND_HOSTNAME=hostname.us-central1.run.app" --set-env-vars "VITE_SEGMENT_API_URL=https://cdn.segment.com/v1/projects/4SGwdwzuDm5WkFvQtz7D6ATQlo14yjmW/settings"
source location current directory > Frontend
region : 32 [us-central 1]
Allow unauthenticated request : Yes

# Backend deploy 
gcloud run deploy --set-env-vars "OPENAI_API_KEY = " --set-env-vars "DIFFBOT_API_KEY = " --set-env-vars "NEO4J_URI = " --set-env-vars "NEO4J_PASSWORD = " --set-env-vars "NEO4J_USERNAME = "
source location current directory > Backend
region : 32 [us-central 1]
Allow unauthenticated request : Yes

ENV

LLMs Supported

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

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🦙3

docker exec -it ollama ollama run llama3

4. Configure env variable in docker compose or backend environment.

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.

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.

Links

LLM Knowledge Graph Builder Application

Neo4j Workspace

Reference

Demo of application

Contact

For any inquiries or support, feel free to raise Github Issue

Happy Graph Building!