Knowledge Graph Builder App

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.

Getting started

Run Docker Compose to build and start all components:
```
docker-compose up --build
```

Alternatively, you can run specific directories separately:

For the frontend:
```
cd frontend
yarn
yarn run dev
```

For the backend:

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

Features

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

Setting up Environment Variables

Create .env file and update the following env variables.
OPENAI_API_KEY = ""
DIFFBOT_API_KEY = ""
NEO4J_URI = ""
NEO4J_USERNAME = ""
NEO4J_PASSWORD = ""
AWS_ACCESS_KEY_ID = ""
AWS_SECRET_ACCESS_KEY = ""
EMBEDDING_MODEL = ""
IS_EMBEDDING = "TRUE" KNN_MIN_SCORE = ""\

Functions/Modules

extract_graph_from_file(uri, userName, password, file_path, 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.

create_source_node_graph(uri, userName, password, file):

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

get_source_list_from_graph():

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

Chunk nodes and embeddings creation in Neo4j

Application Walkthrough

KGB.mp4

Links

The Public Google cloud Run URL. Workspace URL

neo4j-labs / llm-graph-builder