This application is designed to convert PDF documents into a knowledge graph stored in Neo4j. It utilizes the power of OpenAI's GPT/Diffbot LLM(Large language model) to extract nodes, relationships and properties from the text content of the PDF and then organizes them into a structured knowledge graph using Langchain framework. Files can be uploaded from local machine or S3 bucket and then LLM model can be chosen to create the knowledge graph.

⚠️ 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. ⚠️

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):

OPENAI_API_KEY="your-openai-key"
DIFFBOT_API_KEY="your-diffbot-key"

if you only want OpenAI:

LLM_MODELS="OpenAI GPT 3.5,OpenAI GPT 4o"
OPENAI_API_KEY="your-openai-key"

if you only want Diffbot:

LLM_MODELS="Diffbot"
DIFFBOT_API_KEY="your-diffbot-key"

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

docker-compose up --build

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

REACT_APP_SOURCES="local,youtube,wiki,s3"

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

REACT_APP_SOURCES="local,youtube,wiki,s3,gcs"
GOOGLE_CLIENT_ID="xxxx"

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

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.
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

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

# Frontend deploy 
gcloud run deploy 
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

• PDF Upload: Users can upload PDF documents using the Drop Zone.
• S3 Bucket Integration: Users can also specify PDF documents stored in an S3 bucket for processing.
• Knowledge Graph Generation: The application employs OpenAI/Diffbot's LLM to extract relevant information from the PDFs and construct a knowledge graph.
• Neo4j Integration: The extracted nodes and relationships are stored in a Neo4j database for easy visualization and querying.
• Grid View of source node files with : Name,Type,Size,Nodes,Relations,Duration,Status,Source,Model

Extracts nodes , relationships and properties from a PDF file leveraging LLM models.

Args:
 uri: URI of the graph to extract
 userName: Username to use for graph creation ( if None will use username from config file )
 password: Password to use for graph creation ( if None will use password from config file )
 file: File object containing the PDF file path to be used
 model: Type of model to use ('Gemini Pro' or 'Diffbot')

 Returns: 
 Json response to API with fileName, nodeCount, relationshipCount, processingTime, 
 status and model as attributes.

Creates a source node in Neo4jGraph and sets properties.

Args:
 uri: URI of Graph Service to connect to
 userName: Username to connect to Graph Service with ( default : None )
 password: Password to connect to Graph Service with ( default : None )
 file: File object with information about file to be added

Returns: 
 Success or Failure message of node creation

 Returns a list of file sources in the database by querying the graph and 
 sorting the list by the last updated date. 

The Public Google cloud Run URL. Workspace URL