

Llm Graph Builder
Overview :
llm-graph-builder is an application that utilizes large language models (like OpenAI, Gemini, etc.) to extract nodes, relationships, and their attributes from unstructured data (PDFs, DOCS, TXTs, YouTube videos, webpages, etc.) and uses the Langchain framework to create structured knowledge graphs. It supports uploading files from local machines, GCS or S3 buckets, or network resources, selecting an LLM model, and generating knowledge graphs.
Target Users :
Targeted at data scientists, developers, and any professional who needs to extract information from a large amount of unstructured data and build knowledge graphs. This product is tailored for them as it simplifies the process of extracting useful information from complex data sources, enhances efficiency, and promotes the visualization and analysis of knowledge.
Use Cases
Extract key concepts from academic papers and build domain knowledge graphs.
Analyze social media data to identify trends and influential individuals.
Integrate internal corporate documents to build an internal knowledge management system.
Features
Knowledge Graph Creation: Convert unstructured data into structured knowledge graphs using LLMs.
Patterns: Generate graphs with custom patterns or existing patterns from the settings.
View Graphs: View graphs from specific or multiple sources in Bloom.
Interactive Data Dialog: Interact with data in the Neo4j database through conversational queries and retrieve metadata about the source of the query responses.
Local Deployment: Run through docker-compose, supporting OpenAI and Diffbot.
Cloud Deployment: Support deploying applications and packages on Google Cloud Platform.
Environment Configuration: Configure environment variables as needed to enable specific features or integrations.
How to Use
1. Ensure you have Neo4j database version 5.15 or higher and install APOC.
2. Create and configure the .env file, entering the necessary API keys.
3. Select the file source, such as local files, YouTube videos, or webpages.
4. Upload files to the system and select an LLM model for knowledge graph generation.
5. View and analyze the generated knowledge graph in Bloom.
6. Interact with the database through a chatbot, ask questions, and get answers.
7. Adjust environment variables and configurations as needed to adapt to different deployment environments.
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